{smcl}
{com}{sf}{ul off}{txt}{.-}
      name:  {res}<unnamed>
       {txt}log:  {res}/Users/dstegmue/Library/CloudStorage/Dropbox/COVID_experiment_replication/PUBLISH/2_code/replication_log.smcl
  {txt}log type:  {res}smcl
 {txt}opened on:  {res} 7 Sep 2023, 09:36:23
{txt}
{com}. 
. do "2_code/tables_main_paper.do"
{txt}
{com}. /*** *** *** *** *** *** *** *** *** *** *** *** *** *** *** *** *** ***
> Author: Nicolas Longuet Marx
> Date: June 20 2020
> Last modified: Aug 19 2023
> Object: Produce tables of the main paper
> Databases in input: A_experiment.dta
> Databases in output: None
> 
> Tables generated in this script:
> 
> - Table 2: Impact on overall satisfaction with the head of government
> - Table 3: Impact on satisfaction with democracy
> - Table 4: Impact on support for democratic ideals
> 
> *** *** *** *** *** *** *** *** *** *** *** *** *** *** *** *** *** ****/
. 
. use "1_data/A_experiment.dta", replace
{txt}
{com}. 
. /* program to export estimation results better */
. qui do "2_code/mylincom_program.do"
{txt}
{com}. qui do "2_code/get_mean_program.do"
{txt}
{com}. qui do "2_code/mylincom_program_2sls.do"
{txt}
{com}. 
. 
. /* CONTROLS AND MISSING VALUES */
. global controls "thirties fourties fifties sixties seventies income2quartile income3quartile income4quartile incomenoanswer female highschool college noreligion christiannotcatholic catholic fulltimeworker parttimeworker unemployed selfemployed outofLF goodhealth white black latino asian whitecollar bluecollar serviceworker"
{txt}
{com}. 
. 
. global mv_controls
{txt}
{com}. foreach y of global controls {c -(}
{txt}  2{com}.         global mv_controls $mv_controls mv_`y'
{txt}  3{com}.         gen mv_`y' = (`y' == .)
{txt}  4{com}.         replace `y' = 0 if `y' == .
{txt}  5{com}. {c )-}
{txt}(0 real changes made)
(0 real changes made)
(0 real changes made)
(0 real changes made)
(0 real changes made)
(0 real changes made)
(0 real changes made)
(0 real changes made)
(3,016 real changes made)
(0 real changes made)
(1,361 real changes made)
(1,361 real changes made)
(1,124 real changes made)
(1,124 real changes made)
(1,124 real changes made)
(11,592 real changes made)
(20,673 real changes made)
(11,592 real changes made)
(21,100 real changes made)
(11,592 real changes made)
(11 real changes made)
(16,534 real changes made)
(16,534 real changes made)
(16,534 real changes made)
(16,534 real changes made)
(14,538 real changes made)
(14,538 real changes made)
(14,538 real changes made)

{com}. 
. foreach var of varlist $controls $mv_controls  {c -(}
{txt}  2{com}.         assert `var'!= .
{txt}  3{com}. {c )-}
{txt}
{com}. 
. 
. * Check that treatment is well defined
. foreach var of varlist blame* praisi* bad_* TH* TE* treatc* healthc* econc* {c -(}
{txt}  2{com}.         di "`var'"
{txt}  3{com}.         assert `var'!= .
{txt}  4{com}. {c )-}
blame_gov_health
blame_gov_econ
praising_gov_health
praising_gov_econ
bad_econ_situation
bad_health_situation
TH1
TH2
TH3
TH4
TH1_TE1
TH1_TE2
TH1_TE3
TH1_TE4
TH2_TE1
TH2_TE2
TH2_TE3
TH2_TE4
TH3_TE1
TH3_TE2
TH3_TE3
TH3_TE4
TH4_TE1
TH4_TE2
TH4_TE3
TH4_TE4
TE1
TE2
TE3
TE4
treatc
healthc
econc
{txt}
{com}.         
. la var eval_eco "Economic satisfaction"
{txt}
{com}. la var eval_sant "Health satisfaction"  
{txt}
{com}. 
. **# Table 2: Impact on overall satisfaction with the head of government 
. 
. /* 2. Col 1. 16 Instruments */
. eststo clear
{txt}
{com}. eststo: ivreg2 satis_head ///
>         (eval_eco eval_sant = TH1_TE1 TH1_TE2 TH1_TE3 TH1_TE4 TH2_TE1 TH2_TE2 TH2_TE3 TH2_TE4 TH3_TE1 TH3_TE2 TH3_TE3 TH3_TE4 TH4_TE1 TH4_TE2 TH4_TE3), small  robust
{res}
{txt}IV (2SLS) estimation
{hline 20}

Estimates efficient for homoskedasticity only
Statistics robust to heteroskedasticity

{col 55}Number of obs = {res}   22541
{txt}{col 55}F(  2, 22538) = {res}   20.18
{txt}{col 55}Prob > F      = {res}  0.0000
{txt}Total (centered) SS     = {res} 2223.318237{txt}{col 55}Centered R2   = {res}  0.5992
{txt}Total (uncentered) SS   = {res}  6955.66003{txt}{col 55}Uncentered R2 = {res}  0.8719
{txt}Residual SS             = {res} 891.0241501{txt}{col 55}Root MSE      = {res}   .1988

{txt}{hline 13}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 14}{c |}{col 26}    Robust
{col 1}  satis_head{col 14}{c |} Coefficient{col 26}  std. err.{col 38}      t{col 46}   P>|t|{col 54}     [95% con{col 67}f. interval]
{hline 13}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 4}eval_eco {c |}{col 14}{res}{space 2} .3868669{col 26}{space 2} .1508326{col 37}{space 1}    2.56{col 46}{space 3}0.010{col 54}{space 4} .0912245{col 67}{space 3} .6825093
{txt}{space 3}eval_sant {c |}{col 14}{res}{space 2} .3782275{col 26}{space 2} .1110588{col 37}{space 1}    3.41{col 46}{space 3}0.001{col 54}{space 4} .1605446{col 67}{space 3} .5959104
{txt}{space 7}_cons {c |}{col 14}{res}{space 2} .0716578{col 26}{space 2}  .064552{col 37}{space 1}    1.11{col 46}{space 3}0.267{col 54}{space 4}-.0548686{col 67}{space 3} .1981842
{txt}{hline 13}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{help ivreg2##idtest:Underidentification test} (Kleibergen-Paap rk LM statistic):{res}{col 71}  38.054
{txt}{col 52}Chi-sq({res}14{txt}) P-val =  {res}{col 73}0.0005
{txt}{hline 78}
{help ivreg2##widtest:Weak identification test} (Cragg-Donald Wald F statistic):{res}{col 71}   2.511
{txt}                         (Kleibergen-Paap rk Wald F statistic):{res}{col 71}   2.550
{txt}Stock-Yogo weak ID test critical values:{res}{txt}{col 42} 5% maximal IV relative bias{res}{col 73} 19.98
{txt}{col 42}10% maximal IV relative bias{res}{col 73} 10.93
{txt}{col 42}20% maximal IV relative bias{res}{col 73}  6.19
{txt}{col 42}30% maximal IV relative bias{res}{col 73}  4.50
{txt}{col 42}10% maximal IV size{res}{col 73} 38.08
{txt}{col 42}15% maximal IV size{res}{col 73} 20.60
{txt}{col 42}20% maximal IV size{res}{col 73} 14.65
{txt}{col 42}25% maximal IV size{res}{col 73} 11.58
{txt}Source: Stock-Yogo (2005).  Reproduced by permission.
NB: Critical values are for Cragg-Donald F statistic and i.i.d. errors.
{hline 78}
{help ivreg2##overidtests:Hansen J statistic} (overidentification test of all instruments):{res}{col 71}   8.421
{txt}{col 52}Chi-sq({res}13{txt}) P-val =  {res}{col 73}0.8151
{txt}{hline 78}
Instrumented:{col 23}eval_eco eval_sant
Excluded instruments:{col 23}TH1_TE1 TH1_TE2 TH1_TE3 TH1_TE4 TH2_TE1 TH2_TE2 TH2_TE3
{col 23}TH2_TE4 TH3_TE1 TH3_TE2 TH3_TE3 TH3_TE4 TH4_TE1 TH4_TE2
{col 23}TH4_TE3
{hline 78}
({res}est1{txt} stored)

{com}. 
. * Note: here and later, we report the Cragg-Donald statistic from the ivreg2 regression. We are following the ivreg2 helper and take the correspondingly-robust Kleibergen-Paap since we are using the robust command. 
. estadd scalar fstat = e(widstat)

{txt}added scalar:
              e(fstat) =  {res}2.5499264
{txt}
{com}. mylincom_2sls

{p 0 7}{space 1}{text:( 1)}{space 1} {res}eval_eco - eval_sant = 0{p_end}

{txt}{hline 13}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 1}  satis_head{col 14}{c |} Coefficient{col 26}  Std. err.{col 38}      t{col 46}   P>|t|{col 54}     [95% con{col 67}f. interval]
{hline 13}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 9}(1) {c |}{col 14}{res}{space 2} .0086394{col 26}{space 2} .2317505{col 37}{space 1}    0.04{col 46}{space 3}0.970{col 54}{space 4}-.4456076{col 67}{space 3} .4628864
{txt}{hline 13}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}

added macro:
             e(diffeh) : "{res:0.009}"

added scalar:
           e(diffehsd) =  {res}.23175048
{txt}
{com}. estadd local iv "16 IVs"

{txt}added macro:
                 e(iv) : "{res:16 IVs}"

{com}. get_mean

{txt}added scalar:
                 e(av) =  {res}.458
{txt}
{com}. 
. /* 2. Col 2. 16 Instruments + controls */
. eststo: ivreg2 satis_head ///
>         (eval_eco eval_sant = TH1_TE1 TH1_TE2 TH1_TE3 TH1_TE4 TH2_TE1 TH2_TE2 TH2_TE3 TH2_TE4 TH3_TE1 TH3_TE2 TH3_TE3 TH3_TE4 TH4_TE1 TH4_TE2 TH4_TE3) $controls $mv_controls  i.country, small robust 
{txt}Warning - collinearities detected
Vars dropped:{col 21}mv_thirties mv_fourties mv_fifties mv_sixties mv_seventies
{col 21}mv_income2quartile mv_income3quartile mv_income4quartile
{col 21}mv_female mv_college mv_christiannotcatholic mv_catholic
{col 21}mv_fulltimeworker mv_unemployed mv_outofLF mv_black
{col 21}mv_latino mv_asian mv_bluecollar mv_serviceworker 4.country
{col 21}7.country 9.country 11.country 12.country
{res}
{txt}IV (2SLS) estimation
{hline 20}

Estimates efficient for homoskedasticity only
Statistics robust to heteroskedasticity

{col 55}Number of obs = {res}   22541
{txt}{col 55}F( 44, 22496) = {res}  192.10
{txt}{col 55}Prob > F      = {res}  0.0000
{txt}Total (centered) SS     = {res} 2223.318237{txt}{col 55}Centered R2   = {res}  0.6310
{txt}Total (uncentered) SS   = {res}  6955.66003{txt}{col 55}Uncentered R2 = {res}  0.8821
{txt}Residual SS             = {res} 820.3318599{txt}{col 55}Root MSE      = {res}    .191

{txt}{hline 24}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 25}{c |}{col 37}    Robust
{col 1}             satis_head{col 25}{c |} Coefficient{col 37}  std. err.{col 49}      t{col 57}   P>|t|{col 65}     [95% con{col 78}f. interval]
{hline 24}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 15}eval_eco {c |}{col 25}{res}{space 2} .3939531{col 37}{space 2} .1450997{col 48}{space 1}    2.72{col 57}{space 3}0.007{col 65}{space 4} .1095476{col 78}{space 3} .6783586
{txt}{space 14}eval_sant {c |}{col 25}{res}{space 2} .3767773{col 37}{space 2}  .105632{col 48}{space 1}    3.57{col 57}{space 3}0.000{col 65}{space 4} .1697311{col 78}{space 3} .5838234
{txt}{space 15}thirties {c |}{col 25}{res}{space 2}-.0018482{col 37}{space 2} .0048462{col 48}{space 1}   -0.38{col 57}{space 3}0.703{col 65}{space 4}-.0113472{col 78}{space 3} .0076507
{txt}{space 15}fourties {c |}{col 25}{res}{space 2}-.0141033{col 37}{space 2} .0052251{col 48}{space 1}   -2.70{col 57}{space 3}0.007{col 65}{space 4}-.0243449{col 78}{space 3}-.0038617
{txt}{space 16}fifties {c |}{col 25}{res}{space 2}-.0199998{col 37}{space 2} .0051329{col 48}{space 1}   -3.90{col 57}{space 3}0.000{col 65}{space 4}-.0300606{col 78}{space 3} -.009939
{txt}{space 16}sixties {c |}{col 25}{res}{space 2}-.0299297{col 37}{space 2} .0050564{col 48}{space 1}   -5.92{col 57}{space 3}0.000{col 65}{space 4}-.0398407{col 78}{space 3}-.0200187
{txt}{space 14}seventies {c |}{col 25}{res}{space 2}-.0194612{col 37}{space 2} .0065669{col 48}{space 1}   -2.96{col 57}{space 3}0.003{col 65}{space 4}-.0323329{col 78}{space 3}-.0065896
{txt}{space 8}income2quartile {c |}{col 25}{res}{space 2} .0014005{col 37}{space 2} .0046515{col 48}{space 1}    0.30{col 57}{space 3}0.763{col 65}{space 4}-.0077167{col 78}{space 3} .0105178
{txt}{space 8}income3quartile {c |}{col 25}{res}{space 2} .0069309{col 37}{space 2} .0049083{col 48}{space 1}    1.41{col 57}{space 3}0.158{col 65}{space 4}-.0026898{col 78}{space 3} .0165516
{txt}{space 8}income4quartile {c |}{col 25}{res}{space 2}   .00984{col 37}{space 2} .0066018{col 48}{space 1}    1.49{col 57}{space 3}0.136{col 65}{space 4}-.0031001{col 78}{space 3}   .02278
{txt}{space 9}incomenoanswer {c |}{col 25}{res}{space 2}-.0017783{col 37}{space 2}  .006233{col 48}{space 1}   -0.29{col 57}{space 3}0.775{col 65}{space 4}-.0139954{col 78}{space 3} .0104387
{txt}{space 17}female {c |}{col 25}{res}{space 2} .0105124{col 37}{space 2}  .002621{col 48}{space 1}    4.01{col 57}{space 3}0.000{col 65}{space 4} .0053751{col 78}{space 3} .0156498
{txt}{space 13}highschool {c |}{col 25}{res}{space 2} .0056058{col 37}{space 2} .0043316{col 48}{space 1}    1.29{col 57}{space 3}0.196{col 65}{space 4}-.0028844{col 78}{space 3}  .014096
{txt}{space 16}college {c |}{col 25}{res}{space 2}-.0005643{col 37}{space 2} .0039985{col 48}{space 1}   -0.14{col 57}{space 3}0.888{col 65}{space 4}-.0084015{col 78}{space 3}  .007273
{txt}{space 13}noreligion {c |}{col 25}{res}{space 2}-.0191923{col 37}{space 2} .0059499{col 48}{space 1}   -3.23{col 57}{space 3}0.001{col 65}{space 4}-.0308545{col 78}{space 3}-.0075302
{txt}{space 3}christiannotcatholic {c |}{col 25}{res}{space 2} .0297251{col 37}{space 2} .0081716{col 48}{space 1}    3.64{col 57}{space 3}0.000{col 65}{space 4} .0137082{col 78}{space 3} .0457419
{txt}{space 15}catholic {c |}{col 25}{res}{space 2} .0052878{col 37}{space 2} .0061839{col 48}{space 1}    0.86{col 57}{space 3}0.393{col 65}{space 4}-.0068332{col 78}{space 3} .0174087
{txt}{space 9}fulltimeworker {c |}{col 25}{res}{space 2}-.1788494{col 37}{space 2} .0208956{col 48}{space 1}   -8.56{col 57}{space 3}0.000{col 65}{space 4}-.2198062{col 78}{space 3}-.1378926
{txt}{space 9}parttimeworker {c |}{col 25}{res}{space 2}-.1768134{col 37}{space 2} .0251104{col 48}{space 1}   -7.04{col 57}{space 3}0.000{col 65}{space 4}-.2260315{col 78}{space 3}-.1275953
{txt}{space 13}unemployed {c |}{col 25}{res}{space 2}-.1915381{col 37}{space 2} .0248346{col 48}{space 1}   -7.71{col 57}{space 3}0.000{col 65}{space 4}-.2402156{col 78}{space 3}-.1428605
{txt}{space 11}selfemployed {c |}{col 25}{res}{space 2}-.2200253{col 37}{space 2} .0331854{col 48}{space 1}   -6.63{col 57}{space 3}0.000{col 65}{space 4} -.285071{col 78}{space 3}-.1549796
{txt}{space 16}outofLF {c |}{col 25}{res}{space 2} -.184518{col 37}{space 2} .0218418{col 48}{space 1}   -8.45{col 57}{space 3}0.000{col 65}{space 4}-.2273296{col 78}{space 3}-.1417065
{txt}{space 13}goodhealth {c |}{col 25}{res}{space 2}   .01584{col 37}{space 2} .0057988{col 48}{space 1}    2.73{col 57}{space 3}0.006{col 65}{space 4}  .004474{col 78}{space 3}  .027206
{txt}{space 18}white {c |}{col 25}{res}{space 2} .1048411{col 37}{space 2} .0347089{col 48}{space 1}    3.02{col 57}{space 3}0.003{col 65}{space 4} .0368093{col 78}{space 3}  .172873
{txt}{space 18}black {c |}{col 25}{res}{space 2}-.0196447{col 37}{space 2} .0382316{col 48}{space 1}   -0.51{col 57}{space 3}0.607{col 65}{space 4}-.0945814{col 78}{space 3} .0552919
{txt}{space 17}latino {c |}{col 25}{res}{space 2} .0014085{col 37}{space 2} .0413792{col 48}{space 1}    0.03{col 57}{space 3}0.973{col 65}{space 4}-.0796976{col 78}{space 3} .0825145
{txt}{space 18}asian {c |}{col 25}{res}{space 2} .0384938{col 37}{space 2} .0387419{col 48}{space 1}    0.99{col 57}{space 3}0.320{col 65}{space 4}-.0374429{col 78}{space 3} .1144306
{txt}{space 12}whitecollar {c |}{col 25}{res}{space 2} -.007696{col 37}{space 2} .0071716{col 48}{space 1}   -1.07{col 57}{space 3}0.283{col 65}{space 4}-.0217529{col 78}{space 3} .0063608
{txt}{space 13}bluecollar {c |}{col 25}{res}{space 2}-.0049142{col 37}{space 2} .0077413{col 48}{space 1}   -0.63{col 57}{space 3}0.526{col 65}{space 4}-.0200877{col 78}{space 3} .0102593
{txt}{space 10}serviceworker {c |}{col 25}{res}{space 2}-.0002481{col 37}{space 2} .0061041{col 48}{space 1}   -0.04{col 57}{space 3}0.968{col 65}{space 4}-.0122125{col 78}{space 3} .0117163
{txt}{space 12}mv_thirties {c |}{col 25}{res}{space 2}        0{col 37}{txt}  (omitted)
{space 12}mv_fourties {c |}{col 25}{res}{space 2}        0{col 37}{txt}  (omitted)
{space 13}mv_fifties {c |}{col 25}{res}{space 2}        0{col 37}{txt}  (omitted)
{space 13}mv_sixties {c |}{col 25}{res}{space 2}        0{col 37}{txt}  (omitted)
{space 11}mv_seventies {c |}{col 25}{res}{space 2}        0{col 37}{txt}  (omitted)
{space 5}mv_income2quartile {c |}{col 25}{res}{space 2}        0{col 37}{txt}  (omitted)
{space 5}mv_income3quartile {c |}{col 25}{res}{space 2}        0{col 37}{txt}  (omitted)
{space 5}mv_income4quartile {c |}{col 25}{res}{space 2}        0{col 37}{txt}  (omitted)
{space 6}mv_incomenoanswer {c |}{col 25}{res}{space 2}-.0692738{col 37}{space 2} .0102291{col 48}{space 1}   -6.77{col 57}{space 3}0.000{col 65}{space 4}-.0893236{col 78}{space 3}-.0492239
{txt}{space 14}mv_female {c |}{col 25}{res}{space 2}        0{col 37}{txt}  (omitted)
{space 10}mv_highschool {c |}{col 25}{res}{space 2}-.0080507{col 37}{space 2} .0106373{col 48}{space 1}   -0.76{col 57}{space 3}0.449{col 65}{space 4}-.0289006{col 78}{space 3} .0127992
{txt}{space 13}mv_college {c |}{col 25}{res}{space 2}        0{col 37}{txt}  (omitted)
{space 10}mv_noreligion {c |}{col 25}{res}{space 2}-.0135569{col 37}{space 2} .0181009{col 48}{space 1}   -0.75{col 57}{space 3}0.454{col 65}{space 4} -.049036{col 78}{space 3} .0219222
{txt}mv_christiannotcatholic {c |}{col 25}{res}{space 2}        0{col 37}{txt}  (omitted)
{space 12}mv_catholic {c |}{col 25}{res}{space 2}        0{col 37}{txt}  (omitted)
{space 6}mv_fulltimeworker {c |}{col 25}{res}{space 2}        0{col 37}{txt}  (omitted)
{space 6}mv_parttimeworker {c |}{col 25}{res}{space 2}-.1704866{col 37}{space 2}  .021961{col 48}{space 1}   -7.76{col 57}{space 3}0.000{col 65}{space 4}-.2135317{col 78}{space 3}-.1274414
{txt}{space 10}mv_unemployed {c |}{col 25}{res}{space 2}        0{col 37}{txt}  (omitted)
{space 8}mv_selfemployed {c |}{col 25}{res}{space 2} .1366159{col 37}{space 2} .0276698{col 48}{space 1}    4.94{col 57}{space 3}0.000{col 65}{space 4} .0823812{col 78}{space 3} .1908505
{txt}{space 13}mv_outofLF {c |}{col 25}{res}{space 2}        0{col 37}{txt}  (omitted)
{space 10}mv_goodhealth {c |}{col 25}{res}{space 2} .0507026{col 37}{space 2} .0406542{col 48}{space 1}    1.25{col 57}{space 3}0.212{col 65}{space 4}-.0289825{col 78}{space 3} .1303876
{txt}{space 15}mv_white {c |}{col 25}{res}{space 2} .1670937{col 37}{space 2} .0471455{col 48}{space 1}    3.54{col 57}{space 3}0.000{col 65}{space 4} .0746852{col 78}{space 3} .2595023
{txt}{space 15}mv_black {c |}{col 25}{res}{space 2}        0{col 37}{txt}  (omitted)
{space 14}mv_latino {c |}{col 25}{res}{space 2}        0{col 37}{txt}  (omitted)
{space 15}mv_asian {c |}{col 25}{res}{space 2}        0{col 37}{txt}  (omitted)
{space 9}mv_whitecollar {c |}{col 25}{res}{space 2} .1736705{col 37}{space 2} .0252571{col 48}{space 1}    6.88{col 57}{space 3}0.000{col 65}{space 4} .1241648{col 78}{space 3} .2231762
{txt}{space 10}mv_bluecollar {c |}{col 25}{res}{space 2}        0{col 37}{txt}  (omitted)
{space 7}mv_serviceworker {c |}{col 25}{res}{space 2}        0{col 37}{txt}  (omitted)
{space 23} {c |}
{space 16}country {c |}
{space 15}Austria  {c |}{col 25}{res}{space 2} .1990188{col 37}{space 2} .0596365{col 48}{space 1}    3.34{col 57}{space 3}0.001{col 65}{space 4} .0821271{col 78}{space 3} .3159105
{txt}{space 16}Brazil  {c |}{col 25}{res}{space 2} .0170054{col 37}{space 2}   .01612{col 48}{space 1}    1.05{col 57}{space 3}0.291{col 65}{space 4}-.0145909{col 78}{space 3} .0486017
{txt}{space 16}France  {c |}{col 25}{res}{space 2}        0{col 37}{txt}  (omitted)
{space 15}Germany  {c |}{col 25}{res}{space 2} .2339395{col 37}{space 2} .0600256{col 48}{space 1}    3.90{col 57}{space 3}0.000{col 65}{space 4} .1162852{col 78}{space 3} .3515938
{txt}{space 17}Italy  {c |}{col 25}{res}{space 2} .2756928{col 37}{space 2} .0543854{col 48}{space 1}    5.07{col 57}{space 3}0.000{col 65}{space 4} .1690937{col 78}{space 3}  .382292
{txt}{space 11}New_Zealand  {c |}{col 25}{res}{space 2}        0{col 37}{txt}  (omitted)
{space 16}Poland  {c |}{col 25}{res}{space 2}-.0086358{col 37}{space 2} .0184017{col 48}{space 1}   -0.47{col 57}{space 3}0.639{col 65}{space 4}-.0447045{col 78}{space 3} .0274328
{txt}{space 17}Spain  {c |}{col 25}{res}{space 2}        0{col 37}{txt}  (omitted)
{space 16}Sweden  {c |}{col 25}{res}{space 2} .0427462{col 37}{space 2}  .014926{col 48}{space 1}    2.86{col 57}{space 3}0.004{col 65}{space 4} .0134902{col 78}{space 3} .0720023
{txt}{space 20}UK  {c |}{col 25}{res}{space 2}        0{col 37}{txt}  (omitted)
{space 20}US  {c |}{col 25}{res}{space 2}        0{col 37}{txt}  (omitted)
{space 23} {c |}
{space 18}_cons {c |}{col 25}{res}{space 2} -.094877{col 37}{space 2} .0409508{col 48}{space 1}   -2.32{col 57}{space 3}0.021{col 65}{space 4}-.1751433{col 78}{space 3}-.0146107
{txt}{hline 24}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{help ivreg2##idtest:Underidentification test} (Kleibergen-Paap rk LM statistic):{res}{col 71}  43.180
{txt}{col 52}Chi-sq({res}14{txt}) P-val =  {res}{col 73}0.0001
{txt}{hline 78}
{help ivreg2##widtest:Weak identification test} (Cragg-Donald Wald F statistic):{res}{col 71}   2.827
{txt}                         (Kleibergen-Paap rk Wald F statistic):{res}{col 71}   2.889
{txt}Stock-Yogo weak ID test critical values:{res}{txt}{col 42} 5% maximal IV relative bias{res}{col 73} 19.98
{txt}{col 42}10% maximal IV relative bias{res}{col 73} 10.93
{txt}{col 42}20% maximal IV relative bias{res}{col 73}  6.19
{txt}{col 42}30% maximal IV relative bias{res}{col 73}  4.50
{txt}{col 42}10% maximal IV size{res}{col 73} 38.08
{txt}{col 42}15% maximal IV size{res}{col 73} 20.60
{txt}{col 42}20% maximal IV size{res}{col 73} 14.65
{txt}{col 42}25% maximal IV size{res}{col 73} 11.58
{txt}Source: Stock-Yogo (2005).  Reproduced by permission.
NB: Critical values are for Cragg-Donald F statistic and i.i.d. errors.
{hline 78}
{help ivreg2##overidtests:Hansen J statistic} (overidentification test of all instruments):{res}{col 71}   9.081
{txt}{col 52}Chi-sq({res}13{txt}) P-val =  {res}{col 73}0.7668
{txt}{hline 78}
Instrumented:{col 23}eval_eco eval_sant
Included instruments:{col 23}thirties fourties fifties sixties seventies
{col 23}income2quartile income3quartile income4quartile
{col 23}incomenoanswer female highschool college noreligion
{col 23}christiannotcatholic catholic fulltimeworker
{col 23}parttimeworker unemployed selfemployed outofLF goodhealth
{col 23}white black latino asian whitecollar bluecollar
{col 23}serviceworker mv_incomenoanswer mv_highschool
{col 23}mv_noreligion mv_parttimeworker mv_selfemployed
{col 23}mv_goodhealth mv_white mv_whitecollar 2.country 3.country
{col 23}5.country 6.country 8.country 10.country
Excluded instruments:{col 23}TH1_TE1 TH1_TE2 TH1_TE3 TH1_TE4 TH2_TE1 TH2_TE2 TH2_TE3
{col 23}TH2_TE4 TH3_TE1 TH3_TE2 TH3_TE3 TH3_TE4 TH4_TE1 TH4_TE2
{col 23}TH4_TE3
Dropped collinear:{col 23}mv_thirties mv_fourties mv_fifties mv_sixties
{col 23}mv_seventies mv_income2quartile mv_income3quartile
{col 23}mv_income4quartile mv_female mv_college
{col 23}mv_christiannotcatholic mv_catholic mv_fulltimeworker
{col 23}mv_unemployed mv_outofLF mv_black mv_latino mv_asian
{col 23}mv_bluecollar mv_serviceworker 4.country 7.country
{col 23}9.country 11.country 12.country
{hline 78}
({res}est2{txt} stored)

{com}. 
. estadd scalar fstat = e(widstat)

{txt}added scalar:
              e(fstat) =  {res}2.8885572
{txt}
{com}. mylincom_2sls

{p 0 7}{space 1}{text:( 1)}{space 1} {res}eval_eco - eval_sant = 0{p_end}

{txt}{hline 13}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 1}  satis_head{col 14}{c |} Coefficient{col 26}  Std. err.{col 38}      t{col 46}   P>|t|{col 54}     [95% con{col 67}f. interval]
{hline 13}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 9}(1) {c |}{col 14}{res}{space 2} .0171759{col 26}{space 2} .2227362{col 37}{space 1}    0.08{col 46}{space 3}0.939{col 54}{space 4}-.4194025{col 67}{space 3} .4537542
{txt}{hline 13}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}

added macro:
             e(diffeh) : "{res:0.017}"

added scalar:
           e(diffehsd) =  {res}.22273617
{txt}
{com}. estadd local iv "16 IVs"

{txt}added macro:
                 e(iv) : "{res:16 IVs}"

{com}. estadd local CFE "X"

{txt}added macro:
                e(CFE) : "{res:X}"

{com}. estadd local Controls "X", replace

{txt}added macro:
           e(Controls) : "{res:X}"

{com}. get_mean

{txt}added scalar:
                 e(av) =  {res}.458
{txt}
{com}. 
. /* 2. Col 3. 2 Instruments */
. eststo: ivreg2 satis_head ///
>         (eval_eco eval_sant = healthc econc) , small robust 
{res}
{txt}IV (2SLS) estimation
{hline 20}

Estimates efficient for homoskedasticity only
Statistics robust to heteroskedasticity

{col 55}Number of obs = {res}   22541
{txt}{col 55}F(  2, 22538) = {res}   10.81
{txt}{col 55}Prob > F      = {res}  0.0000
{txt}Total (centered) SS     = {res} 2223.318237{txt}{col 55}Centered R2   = {res}  0.5776
{txt}Total (uncentered) SS   = {res}  6955.66003{txt}{col 55}Uncentered R2 = {res}  0.8650
{txt}Residual SS             = {res} 939.1240687{txt}{col 55}Root MSE      = {res}   .2041

{txt}{hline 13}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 14}{c |}{col 26}    Robust
{col 1}  satis_head{col 14}{c |} Coefficient{col 26}  std. err.{col 38}      t{col 46}   P>|t|{col 54}     [95% con{col 67}f. interval]
{hline 13}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 4}eval_eco {c |}{col 14}{res}{space 2} .3039951{col 26}{space 2} .1779834{col 37}{space 1}    1.71{col 46}{space 3}0.088{col 54}{space 4}-.0448646{col 67}{space 3} .6528549
{txt}{space 3}eval_sant {c |}{col 14}{res}{space 2} .3903469{col 26}{space 2} .1275958{col 37}{space 1}    3.06{col 46}{space 3}0.002{col 54}{space 4} .1402504{col 67}{space 3} .6404435
{txt}{space 7}_cons {c |}{col 14}{res}{space 2}  .107065{col 26}{space 2} .0828827{col 37}{space 1}    1.29{col 46}{space 3}0.196{col 54}{space 4}-.0553909{col 67}{space 3} .2695209
{txt}{hline 13}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{help ivreg2##idtest:Underidentification test} (Kleibergen-Paap rk LM statistic):{res}{col 71}  24.373
{txt}{col 52}Chi-sq({res}1{txt}) P-val =  {res}{col 73}0.0000
{txt}{hline 78}
{help ivreg2##widtest:Weak identification test} (Cragg-Donald Wald F statistic):{res}{col 71}  12.180
{txt}                         (Kleibergen-Paap rk Wald F statistic):{res}{col 71}  12.221
{txt}Stock-Yogo weak ID test critical values:{res}{txt}{col 42}10% maximal IV size{res}{col 73}  7.03
{txt}{col 42}15% maximal IV size{res}{col 73}  4.58
{txt}{col 42}20% maximal IV size{res}{col 73}  3.95
{txt}{col 42}25% maximal IV size{res}{col 73}  3.63
{txt}Source: Stock-Yogo (2005).  Reproduced by permission.
NB: Critical values are for Cragg-Donald F statistic and i.i.d. errors.
{hline 78}
{help ivreg2##overidtests:Hansen J statistic} (overidentification test of all instruments):{res}{col 71}   0.000
{txt}{col 50}(equation exactly identified)
{hline 78}
Instrumented:{col 23}eval_eco eval_sant
Excluded instruments:{col 23}healthc econc
{hline 78}
({res}est3{txt} stored)

{com}. 
. estadd scalar fstat = e(widstat)

{txt}added scalar:
              e(fstat) =  {res}12.220785
{txt}
{com}. mylincom_2sls

{p 0 7}{space 1}{text:( 1)}{space 1} {res}eval_eco - eval_sant = 0{p_end}

{txt}{hline 13}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 1}  satis_head{col 14}{c |} Coefficient{col 26}  Std. err.{col 38}      t{col 46}   P>|t|{col 54}     [95% con{col 67}f. interval]
{hline 13}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 9}(1) {c |}{col 14}{res}{space 2}-.0863518{col 26}{space 2} .2623003{col 37}{space 1}   -0.33{col 46}{space 3}0.742{col 54}{space 4}-.6004785{col 67}{space 3} .4277749
{txt}{hline 13}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}

added macro:
             e(diffeh) : "{res:-0.086}"

added scalar:
           e(diffehsd) =  {res}.26230027
{txt}
{com}. estadd local iv "2 SumIVs"

{txt}added macro:
                 e(iv) : "{res:2 SumIVs}"

{com}. get_mean

{txt}added scalar:
                 e(av) =  {res}.458
{txt}
{com}. 
. /* 2. Col 4. 2 Instruments + controls */
. eststo: ivreg2 satis_head ///
>         (eval_eco eval_sant = healthc econc) $controls $mv_controls  i.country, small robust 
{txt}Warning - collinearities detected
Vars dropped:{col 21}mv_thirties mv_fourties mv_fifties mv_sixties mv_seventies
{col 21}mv_income2quartile mv_income3quartile mv_income4quartile
{col 21}mv_female mv_college mv_christiannotcatholic mv_catholic
{col 21}mv_fulltimeworker mv_unemployed mv_outofLF mv_black
{col 21}mv_latino mv_asian mv_bluecollar mv_serviceworker 4.country
{col 21}7.country 9.country 11.country 12.country
{res}
{txt}IV (2SLS) estimation
{hline 20}

Estimates efficient for homoskedasticity only
Statistics robust to heteroskedasticity

{col 55}Number of obs = {res}   22541
{txt}{col 55}F( 44, 22496) = {res}  180.04
{txt}{col 55}Prob > F      = {res}  0.0000
{txt}Total (centered) SS     = {res} 2223.318237{txt}{col 55}Centered R2   = {res}  0.6118
{txt}Total (uncentered) SS   = {res}  6955.66003{txt}{col 55}Uncentered R2 = {res}  0.8759
{txt}Residual SS             = {res} 863.1399028{txt}{col 55}Root MSE      = {res}   .1959

{txt}{hline 24}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 25}{c |}{col 37}    Robust
{col 1}             satis_head{col 25}{c |} Coefficient{col 37}  std. err.{col 49}      t{col 57}   P>|t|{col 65}     [95% con{col 78}f. interval]
{hline 24}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 15}eval_eco {c |}{col 25}{res}{space 2} .3112591{col 37}{space 2}   .17116{col 48}{space 1}    1.82{col 57}{space 3}0.069{col 65}{space 4}-.0242265{col 78}{space 3} .6467446
{txt}{space 14}eval_sant {c |}{col 25}{res}{space 2} .3833423{col 37}{space 2} .1209833{col 48}{space 1}    3.17{col 57}{space 3}0.002{col 65}{space 4} .1462067{col 78}{space 3} .6204779
{txt}{space 15}thirties {c |}{col 25}{res}{space 2}-.0026261{col 37}{space 2} .0051013{col 48}{space 1}   -0.51{col 57}{space 3}0.607{col 65}{space 4}-.0126249{col 78}{space 3} .0073727
{txt}{space 15}fourties {c |}{col 25}{res}{space 2}-.0154271{col 37}{space 2} .0056692{col 48}{space 1}   -2.72{col 57}{space 3}0.007{col 65}{space 4}-.0265391{col 78}{space 3} -.004315
{txt}{space 16}fifties {c |}{col 25}{res}{space 2}-.0209324{col 37}{space 2} .0054731{col 48}{space 1}   -3.82{col 57}{space 3}0.000{col 65}{space 4}  -.03166{col 78}{space 3}-.0102048
{txt}{space 16}sixties {c |}{col 25}{res}{space 2} -.029811{col 37}{space 2} .0052325{col 48}{space 1}   -5.70{col 57}{space 3}0.000{col 65}{space 4}-.0400671{col 78}{space 3}-.0195549
{txt}{space 14}seventies {c |}{col 25}{res}{space 2} -.017753{col 37}{space 2} .0069757{col 48}{space 1}   -2.54{col 57}{space 3}0.011{col 65}{space 4} -.031426{col 78}{space 3}-.0040801
{txt}{space 8}income2quartile {c |}{col 25}{res}{space 2} .0031034{col 37}{space 2} .0051457{col 48}{space 1}    0.60{col 57}{space 3}0.546{col 65}{space 4}-.0069825{col 78}{space 3} .0131893
{txt}{space 8}income3quartile {c |}{col 25}{res}{space 2} .0087882{col 37}{space 2} .0054242{col 48}{space 1}    1.62{col 57}{space 3}0.105{col 65}{space 4}-.0018436{col 78}{space 3} .0194201
{txt}{space 8}income4quartile {c |}{col 25}{res}{space 2} .0131979{col 37}{space 2} .0077935{col 48}{space 1}    1.69{col 57}{space 3}0.090{col 65}{space 4} -.002078{col 78}{space 3} .0284738
{txt}{space 9}incomenoanswer {c |}{col 25}{res}{space 2}-.0025428{col 37}{space 2} .0064657{col 48}{space 1}   -0.39{col 57}{space 3}0.694{col 65}{space 4}-.0152161{col 78}{space 3} .0101305
{txt}{space 17}female {c |}{col 25}{res}{space 2} .0104175{col 37}{space 2}  .002696{col 48}{space 1}    3.86{col 57}{space 3}0.000{col 65}{space 4} .0051331{col 78}{space 3} .0157019
{txt}{space 13}highschool {c |}{col 25}{res}{space 2} .0057041{col 37}{space 2} .0044561{col 48}{space 1}    1.28{col 57}{space 3}0.201{col 65}{space 4}-.0030301{col 78}{space 3} .0144383
{txt}{space 16}college {c |}{col 25}{res}{space 2}-.0004818{col 37}{space 2} .0041194{col 48}{space 1}   -0.12{col 57}{space 3}0.907{col 65}{space 4}-.0085562{col 78}{space 3} .0075926
{txt}{space 13}noreligion {c |}{col 25}{res}{space 2}-.0205411{col 37}{space 2}  .006269{col 48}{space 1}   -3.28{col 57}{space 3}0.001{col 65}{space 4}-.0328288{col 78}{space 3}-.0082534
{txt}{space 3}christiannotcatholic {c |}{col 25}{res}{space 2} .0326338{col 37}{space 2} .0091573{col 48}{space 1}    3.56{col 57}{space 3}0.000{col 65}{space 4}  .014685{col 78}{space 3} .0505827
{txt}{space 15}catholic {c |}{col 25}{res}{space 2} .0065464{col 37}{space 2} .0065454{col 48}{space 1}    1.00{col 57}{space 3}0.317{col 65}{space 4} -.006283{col 78}{space 3} .0193759
{txt}{space 9}fulltimeworker {c |}{col 25}{res}{space 2}-.1880304{col 37}{space 2} .0258859{col 48}{space 1}   -7.26{col 57}{space 3}0.000{col 65}{space 4}-.2387685{col 78}{space 3}-.1372923
{txt}{space 9}parttimeworker {c |}{col 25}{res}{space 2}-.1856549{col 37}{space 2} .0292895{col 48}{space 1}   -6.34{col 57}{space 3}0.000{col 65}{space 4}-.2430645{col 78}{space 3}-.1282454
{txt}{space 13}unemployed {c |}{col 25}{res}{space 2}-.2031598{col 37}{space 2} .0305561{col 48}{space 1}   -6.65{col 57}{space 3}0.000{col 65}{space 4}-.2630519{col 78}{space 3}-.1432678
{txt}{space 11}selfemployed {c |}{col 25}{res}{space 2}-.2304619{col 37}{space 2} .0377915{col 48}{space 1}   -6.10{col 57}{space 3}0.000{col 65}{space 4}-.3045358{col 78}{space 3} -.156388
{txt}{space 16}outofLF {c |}{col 25}{res}{space 2}-.1944013{col 37}{space 2} .0270865{col 48}{space 1}   -7.18{col 57}{space 3}0.000{col 65}{space 4}-.2474927{col 78}{space 3}-.1413098
{txt}{space 13}goodhealth {c |}{col 25}{res}{space 2} .0187521{col 37}{space 2} .0070915{col 48}{space 1}    2.64{col 57}{space 3}0.008{col 65}{space 4} .0048523{col 78}{space 3} .0326519
{txt}{space 18}white {c |}{col 25}{res}{space 2} .1078648{col 37}{space 2} .0348259{col 48}{space 1}    3.10{col 57}{space 3}0.002{col 65}{space 4} .0396036{col 78}{space 3} .1761259
{txt}{space 18}black {c |}{col 25}{res}{space 2}-.0175603{col 37}{space 2} .0383671{col 48}{space 1}   -0.46{col 57}{space 3}0.647{col 65}{space 4}-.0927625{col 78}{space 3} .0576419
{txt}{space 17}latino {c |}{col 25}{res}{space 2} .0034075{col 37}{space 2} .0416873{col 48}{space 1}    0.08{col 57}{space 3}0.935{col 65}{space 4}-.0783025{col 78}{space 3} .0851175
{txt}{space 18}asian {c |}{col 25}{res}{space 2} .0423912{col 37}{space 2} .0392521{col 48}{space 1}    1.08{col 57}{space 3}0.280{col 65}{space 4}-.0345457{col 78}{space 3} .1193281
{txt}{space 12}whitecollar {c |}{col 25}{res}{space 2}-.0086672{col 37}{space 2}  .007444{col 48}{space 1}   -1.16{col 57}{space 3}0.244{col 65}{space 4} -.023258{col 78}{space 3} .0059236
{txt}{space 13}bluecollar {c |}{col 25}{res}{space 2}-.0065208{col 37}{space 2} .0082206{col 48}{space 1}   -0.79{col 57}{space 3}0.428{col 65}{space 4}-.0226339{col 78}{space 3} .0095922
{txt}{space 10}serviceworker {c |}{col 25}{res}{space 2}-.0012687{col 37}{space 2} .0063715{col 48}{space 1}   -0.20{col 57}{space 3}0.842{col 65}{space 4}-.0137573{col 78}{space 3} .0112199
{txt}{space 12}mv_thirties {c |}{col 25}{res}{space 2}        0{col 37}{txt}  (omitted)
{space 12}mv_fourties {c |}{col 25}{res}{space 2}        0{col 37}{txt}  (omitted)
{space 13}mv_fifties {c |}{col 25}{res}{space 2}        0{col 37}{txt}  (omitted)
{space 13}mv_sixties {c |}{col 25}{res}{space 2}        0{col 37}{txt}  (omitted)
{space 11}mv_seventies {c |}{col 25}{res}{space 2}        0{col 37}{txt}  (omitted)
{space 5}mv_income2quartile {c |}{col 25}{res}{space 2}        0{col 37}{txt}  (omitted)
{space 5}mv_income3quartile {c |}{col 25}{res}{space 2}        0{col 37}{txt}  (omitted)
{space 5}mv_income4quartile {c |}{col 25}{res}{space 2}        0{col 37}{txt}  (omitted)
{space 6}mv_incomenoanswer {c |}{col 25}{res}{space 2}-.0669891{col 37}{space 2} .0110341{col 48}{space 1}   -6.07{col 57}{space 3}0.000{col 65}{space 4}-.0886167{col 78}{space 3}-.0453616
{txt}{space 14}mv_female {c |}{col 25}{res}{space 2}        0{col 37}{txt}  (omitted)
{space 10}mv_highschool {c |}{col 25}{res}{space 2}-.0084727{col 37}{space 2} .0108274{col 48}{space 1}   -0.78{col 57}{space 3}0.434{col 65}{space 4}-.0296951{col 78}{space 3} .0127497
{txt}{space 13}mv_college {c |}{col 25}{res}{space 2}        0{col 37}{txt}  (omitted)
{space 10}mv_noreligion {c |}{col 25}{res}{space 2}-.0104052{col 37}{space 2} .0187919{col 48}{space 1}   -0.55{col 57}{space 3}0.580{col 65}{space 4}-.0472387{col 78}{space 3} .0264282
{txt}mv_christiannotcatholic {c |}{col 25}{res}{space 2}        0{col 37}{txt}  (omitted)
{space 12}mv_catholic {c |}{col 25}{res}{space 2}        0{col 37}{txt}  (omitted)
{space 6}mv_fulltimeworker {c |}{col 25}{res}{space 2}        0{col 37}{txt}  (omitted)
{space 6}mv_parttimeworker {c |}{col 25}{res}{space 2}-.1782292{col 37}{space 2} .0259066{col 48}{space 1}   -6.88{col 57}{space 3}0.000{col 65}{space 4}-.2290079{col 78}{space 3}-.1274505
{txt}{space 10}mv_unemployed {c |}{col 25}{res}{space 2}        0{col 37}{txt}  (omitted)
{space 8}mv_selfemployed {c |}{col 25}{res}{space 2} .1508739{col 37}{space 2} .0337042{col 48}{space 1}    4.48{col 57}{space 3}0.000{col 65}{space 4} .0848114{col 78}{space 3} .2169364
{txt}{space 13}mv_outofLF {c |}{col 25}{res}{space 2}        0{col 37}{txt}  (omitted)
{space 10}mv_goodhealth {c |}{col 25}{res}{space 2} .0599275{col 37}{space 2} .0444559{col 48}{space 1}    1.35{col 57}{space 3}0.178{col 65}{space 4}-.0272093{col 78}{space 3} .1470642
{txt}{space 15}mv_white {c |}{col 25}{res}{space 2} .1866896{col 37}{space 2} .0535675{col 48}{space 1}    3.49{col 57}{space 3}0.000{col 65}{space 4} .0816935{col 78}{space 3} .2916857
{txt}{space 15}mv_black {c |}{col 25}{res}{space 2}        0{col 37}{txt}  (omitted)
{space 14}mv_latino {c |}{col 25}{res}{space 2}        0{col 37}{txt}  (omitted)
{space 15}mv_asian {c |}{col 25}{res}{space 2}        0{col 37}{txt}  (omitted)
{space 9}mv_whitecollar {c |}{col 25}{res}{space 2}  .179852{col 37}{space 2} .0294457{col 48}{space 1}    6.11{col 57}{space 3}0.000{col 65}{space 4} .1221363{col 78}{space 3} .2375677
{txt}{space 10}mv_bluecollar {c |}{col 25}{res}{space 2}        0{col 37}{txt}  (omitted)
{space 7}mv_serviceworker {c |}{col 25}{res}{space 2}        0{col 37}{txt}  (omitted)
{space 23} {c |}
{space 16}country {c |}
{space 15}Austria  {c |}{col 25}{res}{space 2} .2260364{col 37}{space 2}   .07014{col 48}{space 1}    3.22{col 57}{space 3}0.001{col 65}{space 4}  .088557{col 78}{space 3} .3635157
{txt}{space 16}Brazil  {c |}{col 25}{res}{space 2}  .011976{col 37}{space 2} .0173716{col 48}{space 1}    0.69{col 57}{space 3}0.491{col 65}{space 4}-.0220736{col 78}{space 3} .0460256
{txt}{space 16}France  {c |}{col 25}{res}{space 2}        0{col 37}{txt}  (omitted)
{space 15}Germany  {c |}{col 25}{res}{space 2} .2610058{col 37}{space 2} .0707609{col 48}{space 1}    3.69{col 57}{space 3}0.000{col 65}{space 4} .1223094{col 78}{space 3} .3997021
{txt}{space 17}Italy  {c |}{col 25}{res}{space 2} .2849811{col 37}{space 2}  .060119{col 48}{space 1}    4.74{col 57}{space 3}0.000{col 65}{space 4} .1671437{col 78}{space 3} .4028185
{txt}{space 11}New_Zealand  {c |}{col 25}{res}{space 2}        0{col 37}{txt}  (omitted)
{space 16}Poland  {c |}{col 25}{res}{space 2}-.0150348{col 37}{space 2} .0200158{col 48}{space 1}   -0.75{col 57}{space 3}0.453{col 65}{space 4}-.0542671{col 78}{space 3} .0241975
{txt}{space 17}Spain  {c |}{col 25}{res}{space 2}        0{col 37}{txt}  (omitted)
{space 16}Sweden  {c |}{col 25}{res}{space 2} .0416242{col 37}{space 2} .0155583{col 48}{space 1}    2.68{col 57}{space 3}0.007{col 65}{space 4} .0111288{col 78}{space 3} .0721196
{txt}{space 20}UK  {c |}{col 25}{res}{space 2}        0{col 37}{txt}  (omitted)
{space 20}US  {c |}{col 25}{res}{space 2}        0{col 37}{txt}  (omitted)
{space 23} {c |}
{space 18}_cons {c |}{col 25}{res}{space 2}-.0841099{col 37}{space 2} .0430681{col 48}{space 1}   -1.95{col 57}{space 3}0.051{col 65}{space 4}-.1685264{col 78}{space 3} .0003065
{txt}{hline 24}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{help ivreg2##idtest:Underidentification test} (Kleibergen-Paap rk LM statistic):{res}{col 71}  28.142
{txt}{col 52}Chi-sq({res}1{txt}) P-val =  {res}{col 73}0.0000
{txt}{hline 78}
{help ivreg2##widtest:Weak identification test} (Cragg-Donald Wald F statistic):{res}{col 71}  13.962
{txt}                         (Kleibergen-Paap rk Wald F statistic):{res}{col 71}  14.088
{txt}Stock-Yogo weak ID test critical values:{res}{txt}{col 42}10% maximal IV size{res}{col 73}  7.03
{txt}{col 42}15% maximal IV size{res}{col 73}  4.58
{txt}{col 42}20% maximal IV size{res}{col 73}  3.95
{txt}{col 42}25% maximal IV size{res}{col 73}  3.63
{txt}Source: Stock-Yogo (2005).  Reproduced by permission.
NB: Critical values are for Cragg-Donald F statistic and i.i.d. errors.
{hline 78}
{help ivreg2##overidtests:Hansen J statistic} (overidentification test of all instruments):{res}{col 71}   0.000
{txt}{col 50}(equation exactly identified)
{hline 78}
Instrumented:{col 23}eval_eco eval_sant
Included instruments:{col 23}thirties fourties fifties sixties seventies
{col 23}income2quartile income3quartile income4quartile
{col 23}incomenoanswer female highschool college noreligion
{col 23}christiannotcatholic catholic fulltimeworker
{col 23}parttimeworker unemployed selfemployed outofLF goodhealth
{col 23}white black latino asian whitecollar bluecollar
{col 23}serviceworker mv_incomenoanswer mv_highschool
{col 23}mv_noreligion mv_parttimeworker mv_selfemployed
{col 23}mv_goodhealth mv_white mv_whitecollar 2.country 3.country
{col 23}5.country 6.country 8.country 10.country
Excluded instruments:{col 23}healthc econc
Dropped collinear:{col 23}mv_thirties mv_fourties mv_fifties mv_sixties
{col 23}mv_seventies mv_income2quartile mv_income3quartile
{col 23}mv_income4quartile mv_female mv_college
{col 23}mv_christiannotcatholic mv_catholic mv_fulltimeworker
{col 23}mv_unemployed mv_outofLF mv_black mv_latino mv_asian
{col 23}mv_bluecollar mv_serviceworker 4.country 7.country
{col 23}9.country 11.country 12.country
{hline 78}
({res}est4{txt} stored)

{com}.         
. estadd scalar fstat = e(widstat)

{txt}added scalar:
              e(fstat) =  {res}14.087577
{txt}
{com}. mylincom_2sls

{p 0 7}{space 1}{text:( 1)}{space 1} {res}eval_eco - eval_sant = 0{p_end}

{txt}{hline 13}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 1}  satis_head{col 14}{c |} Coefficient{col 26}  Std. err.{col 38}      t{col 46}   P>|t|{col 54}     [95% con{col 67}f. interval]
{hline 13}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 9}(1) {c |}{col 14}{res}{space 2}-.0720833{col 26}{space 2} .2528112{col 37}{space 1}   -0.29{col 46}{space 3}0.776{col 54}{space 4}-.5676108{col 67}{space 3} .4234443
{txt}{hline 13}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}

added macro:
             e(diffeh) : "{res:-0.072}"

added scalar:
           e(diffehsd) =  {res}.25281122
{txt}
{com}. estadd local iv "2 SumIVs"

{txt}added macro:
                 e(iv) : "{res:2 SumIVs}"

{com}. estadd local CFE "X"

{txt}added macro:
                e(CFE) : "{res:X}"

{com}. estadd local Controls "X", replace

{txt}added macro:
           e(Controls) : "{res:X}"

{com}. get_mean

{txt}added scalar:
                 e(av) =  {res}.458
{txt}
{com}. 
. esttab using "3_output/1_main_paper/Tables/Table2.tex", depvar keep(eval_eco eval_sant ) ///
>         order(eval_eco eval_sant ) label replace ///
>         cells(b(star fmt(%15.3fc)) se(par fmt(%15.3fc))) interaction(" $/times$ ") ///
>         star(* 0.10 ** 0.05 *** 0.01) ///
>         stats(Controls CFE N av nothing nothing2 diffeh diffehsd nothing iv fstat , layout(@ @ @ @ @ @ @ (@) @ @ @ ) fmt(%15s %15s %15.0fc %9.3f %9.3f %9.3f %9.3f %9.3f %9.3f %15s %9.3f ) ///
>         labels("Individual controls" "Country FE" Observations "Outcome mean" " " "Linear combination of estimates:" "Difference Economic satisfaction" "-Health satisfaction" " " "Instruments" "Cragg-Donald statistic" )) ///
>         collabels(none) mlabels(none) ///
>         mgroups("Satisfaction with the head of government" , pattern(1 0 0 0) ///
>         prefix(\multicolumn{c -(}@span{c )-}{c -(}c{c )-}{c -(}) suffix({c )-}) span erepeat(\cmidrule(lr){c -(}@span{c )-}))
{res}{txt}(output written to {browse  `"3_output/1_main_paper/Tables/Table2.tex"'})

{com}.         
. 
. **# Table 3: Impact on satisfaction with democracy 
. la var satis_head "Satisfaction with the head of government"
{txt}
{com}. eststo clear
{txt}
{com}. /* 3. Col 1: 16 instruments */
. 
. eststo: ivreg2 satis_dem ///
>         (satis_head = TH1_TE1 TH1_TE2 TH1_TE3 TH1_TE4 TH2_TE1 TH2_TE2 TH2_TE3 TH2_TE4 TH3_TE1 TH3_TE2 TH3_TE3 TH3_TE4 TH4_TE1 TH4_TE2 TH4_TE3), small robust 
{res}
{txt}IV (2SLS) estimation
{hline 20}

Estimates efficient for homoskedasticity only
Statistics robust to heteroskedasticity

{col 55}Number of obs = {res}   22541
{txt}{col 55}F(  1, 22539) = {res}   12.20
{txt}{col 55}Prob > F      = {res}  0.0005
{txt}Total (centered) SS     = {res} 1635.679721{txt}{col 55}Centered R2   = {res}  0.3997
{txt}Total (uncentered) SS   = {res} 7268.530045{txt}{col 55}Uncentered R2 = {res}  0.8649
{txt}Residual SS             = {res} 981.8279294{txt}{col 55}Root MSE      = {res}   .2087

{txt}{hline 13}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 14}{c |}{col 26}    Robust
{col 1}   satis_dem{col 14}{c |} Coefficient{col 26}  std. err.{col 38}      t{col 46}   P>|t|{col 54}     [95% con{col 67}f. interval]
{hline 13}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 2}satis_head {c |}{col 14}{res}{space 2}  .521565{col 26}{space 2}  .149351{col 37}{space 1}    3.49{col 46}{space 3}0.000{col 54}{space 4} .2288268{col 67}{space 3} .8143032
{txt}{space 7}_cons {c |}{col 14}{res}{space 2} .2609145{col 26}{space 2}  .068446{col 37}{space 1}    3.81{col 46}{space 3}0.000{col 54}{space 4} .1267555{col 67}{space 3} .3950734
{txt}{hline 13}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{help ivreg2##idtest:Underidentification test} (Kleibergen-Paap rk LM statistic):{res}{col 71}  19.860
{txt}{col 52}Chi-sq({res}15{txt}) P-val =  {res}{col 73}0.1774
{txt}{hline 78}
{help ivreg2##widtest:Weak identification test} (Cragg-Donald Wald F statistic):{res}{col 71}   1.318
{txt}                         (Kleibergen-Paap rk Wald F statistic):{res}{col 71}   1.328
{txt}Stock-Yogo weak ID test critical values:{res}{txt}{col 42} 5% maximal IV relative bias{res}{col 73} 21.23
{txt}{col 42}10% maximal IV relative bias{res}{col 73} 11.51
{txt}{col 42}20% maximal IV relative bias{res}{col 73}  6.42
{txt}{col 42}30% maximal IV relative bias{res}{col 73}  4.63
{txt}{col 42}10% maximal IV size{res}{col 73} 50.39
{txt}{col 42}15% maximal IV size{res}{col 73} 26.80
{txt}{col 42}20% maximal IV size{res}{col 73} 18.72
{txt}{col 42}25% maximal IV size{res}{col 73} 14.60
{txt}Source: Stock-Yogo (2005).  Reproduced by permission.
NB: Critical values are for Cragg-Donald F statistic and i.i.d. errors.
{hline 78}
{help ivreg2##overidtests:Hansen J statistic} (overidentification test of all instruments):{res}{col 71}   7.648
{txt}{col 52}Chi-sq({res}14{txt}) P-val =  {res}{col 73}0.9068
{txt}{hline 78}
Instrumented:{col 23}satis_head
Excluded instruments:{col 23}TH1_TE1 TH1_TE2 TH1_TE3 TH1_TE4 TH2_TE1 TH2_TE2 TH2_TE3
{col 23}TH2_TE4 TH3_TE1 TH3_TE2 TH3_TE3 TH3_TE4 TH4_TE1 TH4_TE2
{col 23}TH4_TE3
{hline 78}
({res}est1{txt} stored)

{com}. 
. estadd scalar fstat = e(widstat)

{txt}added scalar:
              e(fstat) =  {res}1.3283149
{txt}
{com}. estadd local IV "16 IVs"

{txt}added macro:
                 e(IV) : "{res:16 IVs}"

{com}. estadd local nothing nothing2 = " "

{txt}added macro:
            e(nothing) : "{res:nothing2 = " "}"

{com}. get_mean

{txt}added scalar:
                 e(av) =  {res}.5
{txt}
{com}. 
. /* 3. Col 2: 16 instruments + controls */
. eststo: ivreg2 satis_dem ///
>         (satis_head = TH1_TE1 TH1_TE2 TH1_TE3 TH1_TE4 TH2_TE1 TH2_TE2 TH2_TE3 TH2_TE4 TH3_TE1 TH3_TE2 TH3_TE3 TH3_TE4 TH4_TE1 TH4_TE2 TH4_TE3) $controls $mv_controls  i.country, small robust 
{txt}Warning - collinearities detected
Vars dropped:{col 21}mv_thirties mv_fourties mv_fifties mv_sixties mv_seventies
{col 21}mv_income2quartile mv_income3quartile mv_income4quartile
{col 21}mv_female mv_college mv_christiannotcatholic mv_catholic
{col 21}mv_fulltimeworker mv_unemployed mv_outofLF mv_black
{col 21}mv_latino mv_asian mv_bluecollar mv_serviceworker 4.country
{col 21}7.country 9.country 11.country 12.country
{res}
{txt}IV (2SLS) estimation
{hline 20}

Estimates efficient for homoskedasticity only
Statistics robust to heteroskedasticity

{col 55}Number of obs = {res}   22541
{txt}{col 55}F( 43, 22497) = {res}  114.64
{txt}{col 55}Prob > F      = {res}  0.0000
{txt}Total (centered) SS     = {res} 1635.679721{txt}{col 55}Centered R2   = {res}  0.4331
{txt}Total (uncentered) SS   = {res} 7268.530045{txt}{col 55}Uncentered R2 = {res}  0.8724
{txt}Residual SS             = {res} 927.3110743{txt}{col 55}Root MSE      = {res}    .203

{txt}{hline 24}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 25}{c |}{col 37}    Robust
{col 1}              satis_dem{col 25}{c |} Coefficient{col 37}  std. err.{col 49}      t{col 57}   P>|t|{col 65}     [95% con{col 78}f. interval]
{hline 24}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 13}satis_head {c |}{col 25}{res}{space 2} .5283618{col 37}{space 2} .1425757{col 48}{space 1}    3.71{col 57}{space 3}0.000{col 65}{space 4} .2489036{col 78}{space 3}   .80782
{txt}{space 15}thirties {c |}{col 25}{res}{space 2} -.015411{col 37}{space 2} .0050154{col 48}{space 1}   -3.07{col 57}{space 3}0.002{col 65}{space 4}-.0252415{col 78}{space 3}-.0055806
{txt}{space 15}fourties {c |}{col 25}{res}{space 2} -.011639{col 37}{space 2} .0064623{col 48}{space 1}   -1.80{col 57}{space 3}0.072{col 65}{space 4}-.0243056{col 78}{space 3} .0010276
{txt}{space 16}fifties {c |}{col 25}{res}{space 2}-.0108162{col 37}{space 2} .0068628{col 48}{space 1}   -1.58{col 57}{space 3}0.115{col 65}{space 4}-.0242678{col 78}{space 3} .0026354
{txt}{space 16}sixties {c |}{col 25}{res}{space 2} .0033724{col 37}{space 2} .0070662{col 48}{space 1}    0.48{col 57}{space 3}0.633{col 65}{space 4}-.0104778{col 78}{space 3} .0172225
{txt}{space 14}seventies {c |}{col 25}{res}{space 2} .0202027{col 37}{space 2} .0062979{col 48}{space 1}    3.21{col 57}{space 3}0.001{col 65}{space 4} .0078585{col 78}{space 3}  .032547
{txt}{space 8}income2quartile {c |}{col 25}{res}{space 2} .0192263{col 37}{space 2} .0046975{col 48}{space 1}    4.09{col 57}{space 3}0.000{col 65}{space 4} .0100188{col 78}{space 3} .0284338
{txt}{space 8}income3quartile {c |}{col 25}{res}{space 2} .0257428{col 37}{space 2} .0052717{col 48}{space 1}    4.88{col 57}{space 3}0.000{col 65}{space 4} .0154099{col 78}{space 3} .0360757
{txt}{space 8}income4quartile {c |}{col 25}{res}{space 2}  .041517{col 37}{space 2}  .007253{col 48}{space 1}    5.72{col 57}{space 3}0.000{col 65}{space 4} .0273006{col 78}{space 3} .0557333
{txt}{space 9}incomenoanswer {c |}{col 25}{res}{space 2} .0079892{col 37}{space 2} .0067101{col 48}{space 1}    1.19{col 57}{space 3}0.234{col 65}{space 4} -.005163{col 78}{space 3} .0211415
{txt}{space 17}female {c |}{col 25}{res}{space 2}-.0091862{col 37}{space 2} .0031529{col 48}{space 1}   -2.91{col 57}{space 3}0.004{col 65}{space 4}-.0153661{col 78}{space 3}-.0030063
{txt}{space 13}highschool {c |}{col 25}{res}{space 2} .0054292{col 37}{space 2} .0047662{col 48}{space 1}    1.14{col 57}{space 3}0.255{col 65}{space 4} -.003913{col 78}{space 3} .0147714
{txt}{space 16}college {c |}{col 25}{res}{space 2} .0270696{col 37}{space 2} .0042705{col 48}{space 1}    6.34{col 57}{space 3}0.000{col 65}{space 4} .0186992{col 78}{space 3} .0354401
{txt}{space 13}noreligion {c |}{col 25}{res}{space 2}-.0118266{col 37}{space 2} .0076726{col 48}{space 1}   -1.54{col 57}{space 3}0.123{col 65}{space 4}-.0268655{col 78}{space 3} .0032123
{txt}{space 3}christiannotcatholic {c |}{col 25}{res}{space 2}  .012457{col 37}{space 2} .0115508{col 48}{space 1}    1.08{col 57}{space 3}0.281{col 65}{space 4}-.0101833{col 78}{space 3} .0350973
{txt}{space 15}catholic {c |}{col 25}{res}{space 2} .0164308{col 37}{space 2} .0069826{col 48}{space 1}    2.35{col 57}{space 3}0.019{col 65}{space 4} .0027445{col 78}{space 3} .0301172
{txt}{space 9}fulltimeworker {c |}{col 25}{res}{space 2}-.0331149{col 37}{space 2} .0450563{col 48}{space 1}   -0.73{col 57}{space 3}0.462{col 65}{space 4}-.1214284{col 78}{space 3} .0551986
{txt}{space 9}parttimeworker {c |}{col 25}{res}{space 2}-.0069356{col 37}{space 2} .0470736{col 48}{space 1}   -0.15{col 57}{space 3}0.883{col 65}{space 4}-.0992032{col 78}{space 3}  .085332
{txt}{space 13}unemployed {c |}{col 25}{res}{space 2}-.0438316{col 37}{space 2} .0504433{col 48}{space 1}   -0.87{col 57}{space 3}0.385{col 65}{space 4}-.1427039{col 78}{space 3} .0550408
{txt}{space 11}selfemployed {c |}{col 25}{res}{space 2}-.0211849{col 37}{space 2} .0608035{col 48}{space 1}   -0.35{col 57}{space 3}0.728{col 65}{space 4}-.1403641{col 78}{space 3} .0979942
{txt}{space 16}outofLF {c |}{col 25}{res}{space 2}-.0326967{col 37}{space 2} .0467518{col 48}{space 1}   -0.70{col 57}{space 3}0.484{col 65}{space 4}-.1243334{col 78}{space 3}   .05894
{txt}{space 13}goodhealth {c |}{col 25}{res}{space 2} .0251379{col 37}{space 2} .0077078{col 48}{space 1}    3.26{col 57}{space 3}0.001{col 65}{space 4} .0100301{col 78}{space 3} .0402458
{txt}{space 18}white {c |}{col 25}{res}{space 2}-.0107805{col 37}{space 2} .0402719{col 48}{space 1}   -0.27{col 57}{space 3}0.789{col 65}{space 4}-.0897162{col 78}{space 3} .0681553
{txt}{space 18}black {c |}{col 25}{res}{space 2} .0669688{col 37}{space 2} .0387218{col 48}{space 1}    1.73{col 57}{space 3}0.084{col 65}{space 4}-.0089286{col 78}{space 3} .1428662
{txt}{space 17}latino {c |}{col 25}{res}{space 2} .0547505{col 37}{space 2} .0408108{col 48}{space 1}    1.34{col 57}{space 3}0.180{col 65}{space 4}-.0252414{col 78}{space 3} .1347425
{txt}{space 18}asian {c |}{col 25}{res}{space 2} .0344473{col 37}{space 2} .0414733{col 48}{space 1}    0.83{col 57}{space 3}0.406{col 65}{space 4}-.0468433{col 78}{space 3} .1157378
{txt}{space 12}whitecollar {c |}{col 25}{res}{space 2}  .001631{col 37}{space 2} .0076523{col 48}{space 1}    0.21{col 57}{space 3}0.831{col 65}{space 4}-.0133682{col 78}{space 3} .0166301
{txt}{space 13}bluecollar {c |}{col 25}{res}{space 2}-.0101123{col 37}{space 2} .0081638{col 48}{space 1}   -1.24{col 57}{space 3}0.215{col 65}{space 4} -.026114{col 78}{space 3} .0058893
{txt}{space 10}serviceworker {c |}{col 25}{res}{space 2} -.010438{col 37}{space 2} .0059797{col 48}{space 1}   -1.75{col 57}{space 3}0.081{col 65}{space 4}-.0221586{col 78}{space 3} .0012825
{txt}{space 12}mv_thirties {c |}{col 25}{res}{space 2}        0{col 37}{txt}  (omitted)
{space 12}mv_fourties {c |}{col 25}{res}{space 2}        0{col 37}{txt}  (omitted)
{space 13}mv_fifties {c |}{col 25}{res}{space 2}        0{col 37}{txt}  (omitted)
{space 13}mv_sixties {c |}{col 25}{res}{space 2}        0{col 37}{txt}  (omitted)
{space 11}mv_seventies {c |}{col 25}{res}{space 2}        0{col 37}{txt}  (omitted)
{space 5}mv_income2quartile {c |}{col 25}{res}{space 2}        0{col 37}{txt}  (omitted)
{space 5}mv_income3quartile {c |}{col 25}{res}{space 2}        0{col 37}{txt}  (omitted)
{space 5}mv_income4quartile {c |}{col 25}{res}{space 2}        0{col 37}{txt}  (omitted)
{space 6}mv_incomenoanswer {c |}{col 25}{res}{space 2} .0291219{col 37}{space 2} .0135794{col 48}{space 1}    2.14{col 57}{space 3}0.032{col 65}{space 4} .0025053{col 78}{space 3} .0557385
{txt}{space 14}mv_female {c |}{col 25}{res}{space 2}        0{col 37}{txt}  (omitted)
{space 10}mv_highschool {c |}{col 25}{res}{space 2} .0272225{col 37}{space 2} .0106467{col 48}{space 1}    2.56{col 57}{space 3}0.011{col 65}{space 4} .0063543{col 78}{space 3} .0480908
{txt}{space 13}mv_college {c |}{col 25}{res}{space 2}        0{col 37}{txt}  (omitted)
{space 10}mv_noreligion {c |}{col 25}{res}{space 2} .0005398{col 37}{space 2} .0233189{col 48}{space 1}    0.02{col 57}{space 3}0.982{col 65}{space 4}-.0451669{col 78}{space 3} .0462465
{txt}mv_christiannotcatholic {c |}{col 25}{res}{space 2}        0{col 37}{txt}  (omitted)
{space 12}mv_catholic {c |}{col 25}{res}{space 2}        0{col 37}{txt}  (omitted)
{space 6}mv_fulltimeworker {c |}{col 25}{res}{space 2}        0{col 37}{txt}  (omitted)
{space 6}mv_parttimeworker {c |}{col 25}{res}{space 2}-.0158404{col 37}{space 2} .0436233{col 48}{space 1}   -0.36{col 57}{space 3}0.717{col 65}{space 4}-.1013451{col 78}{space 3} .0696644
{txt}{space 10}mv_unemployed {c |}{col 25}{res}{space 2}        0{col 37}{txt}  (omitted)
{space 8}mv_selfemployed {c |}{col 25}{res}{space 2} .0308075{col 37}{space 2} .0450466{col 48}{space 1}    0.68{col 57}{space 3}0.494{col 65}{space 4}-.0574871{col 78}{space 3}  .119102
{txt}{space 13}mv_outofLF {c |}{col 25}{res}{space 2}        0{col 37}{txt}  (omitted)
{space 10}mv_goodhealth {c |}{col 25}{res}{space 2} .1184423{col 37}{space 2} .0760623{col 48}{space 1}    1.56{col 57}{space 3}0.119{col 65}{space 4}-.0306451{col 78}{space 3} .2675297
{txt}{space 15}mv_white {c |}{col 25}{res}{space 2} .0304671{col 37}{space 2} .0648308{col 48}{space 1}    0.47{col 57}{space 3}0.638{col 65}{space 4}-.0966058{col 78}{space 3} .1575399
{txt}{space 15}mv_black {c |}{col 25}{res}{space 2}        0{col 37}{txt}  (omitted)
{space 14}mv_latino {c |}{col 25}{res}{space 2}        0{col 37}{txt}  (omitted)
{space 15}mv_asian {c |}{col 25}{res}{space 2}        0{col 37}{txt}  (omitted)
{space 9}mv_whitecollar {c |}{col 25}{res}{space 2}-.0238005{col 37}{space 2} .0455043{col 48}{space 1}   -0.52{col 57}{space 3}0.601{col 65}{space 4} -.112992{col 78}{space 3}  .065391
{txt}{space 10}mv_bluecollar {c |}{col 25}{res}{space 2}        0{col 37}{txt}  (omitted)
{space 7}mv_serviceworker {c |}{col 25}{res}{space 2}        0{col 37}{txt}  (omitted)
{space 23} {c |}
{space 16}country {c |}
{space 15}Austria  {c |}{col 25}{res}{space 2} .0413313{col 37}{space 2} .0828504{col 48}{space 1}    0.50{col 57}{space 3}0.618{col 65}{space 4}-.1210612{col 78}{space 3} .2037239
{txt}{space 16}Brazil  {c |}{col 25}{res}{space 2}-.0732144{col 37}{space 2} .0149202{col 48}{space 1}   -4.91{col 57}{space 3}0.000{col 65}{space 4} -.102459{col 78}{space 3}-.0439698
{txt}{space 16}France  {c |}{col 25}{res}{space 2}        0{col 37}{txt}  (omitted)
{space 15}Germany  {c |}{col 25}{res}{space 2} .0201346{col 37}{space 2} .0877115{col 48}{space 1}    0.23{col 57}{space 3}0.818{col 65}{space 4} -.151786{col 78}{space 3} .1920553
{txt}{space 17}Italy  {c |}{col 25}{res}{space 2}-.0710875{col 37}{space 2} .0827082{col 48}{space 1}   -0.86{col 57}{space 3}0.390{col 65}{space 4}-.2332012{col 78}{space 3} .0910263
{txt}{space 11}New_Zealand  {c |}{col 25}{res}{space 2}        0{col 37}{txt}  (omitted)
{space 16}Poland  {c |}{col 25}{res}{space 2}-.0948499{col 37}{space 2} .0145928{col 48}{space 1}   -6.50{col 57}{space 3}0.000{col 65}{space 4}-.1234528{col 78}{space 3} -.066247
{txt}{space 17}Spain  {c |}{col 25}{res}{space 2}        0{col 37}{txt}  (omitted)
{space 16}Sweden  {c |}{col 25}{res}{space 2} .0625265{col 37}{space 2} .0161997{col 48}{space 1}    3.86{col 57}{space 3}0.000{col 65}{space 4}  .030774{col 78}{space 3} .0942791
{txt}{space 20}UK  {c |}{col 25}{res}{space 2}        0{col 37}{txt}  (omitted)
{space 20}US  {c |}{col 25}{res}{space 2}        0{col 37}{txt}  (omitted)
{space 23} {c |}
{space 18}_cons {c |}{col 25}{res}{space 2} .2057792{col 37}{space 2} .0387972{col 48}{space 1}    5.30{col 57}{space 3}0.000{col 65}{space 4} .1297341{col 78}{space 3} .2818243
{txt}{hline 24}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{help ivreg2##idtest:Underidentification test} (Kleibergen-Paap rk LM statistic):{res}{col 71}  23.634
{txt}{col 52}Chi-sq({res}15{txt}) P-val =  {res}{col 73}0.0716
{txt}{hline 78}
{help ivreg2##widtest:Weak identification test} (Cragg-Donald Wald F statistic):{res}{col 71}   1.548
{txt}                         (Kleibergen-Paap rk Wald F statistic):{res}{col 71}   1.578
{txt}Stock-Yogo weak ID test critical values:{res}{txt}{col 42} 5% maximal IV relative bias{res}{col 73} 21.23
{txt}{col 42}10% maximal IV relative bias{res}{col 73} 11.51
{txt}{col 42}20% maximal IV relative bias{res}{col 73}  6.42
{txt}{col 42}30% maximal IV relative bias{res}{col 73}  4.63
{txt}{col 42}10% maximal IV size{res}{col 73} 50.39
{txt}{col 42}15% maximal IV size{res}{col 73} 26.80
{txt}{col 42}20% maximal IV size{res}{col 73} 18.72
{txt}{col 42}25% maximal IV size{res}{col 73} 14.60
{txt}Source: Stock-Yogo (2005).  Reproduced by permission.
NB: Critical values are for Cragg-Donald F statistic and i.i.d. errors.
{hline 78}
{help ivreg2##overidtests:Hansen J statistic} (overidentification test of all instruments):{res}{col 71}   7.601
{txt}{col 52}Chi-sq({res}14{txt}) P-val =  {res}{col 73}0.9091
{txt}{hline 78}
Instrumented:{col 23}satis_head
Included instruments:{col 23}thirties fourties fifties sixties seventies
{col 23}income2quartile income3quartile income4quartile
{col 23}incomenoanswer female highschool college noreligion
{col 23}christiannotcatholic catholic fulltimeworker
{col 23}parttimeworker unemployed selfemployed outofLF goodhealth
{col 23}white black latino asian whitecollar bluecollar
{col 23}serviceworker mv_incomenoanswer mv_highschool
{col 23}mv_noreligion mv_parttimeworker mv_selfemployed
{col 23}mv_goodhealth mv_white mv_whitecollar 2.country 3.country
{col 23}5.country 6.country 8.country 10.country
Excluded instruments:{col 23}TH1_TE1 TH1_TE2 TH1_TE3 TH1_TE4 TH2_TE1 TH2_TE2 TH2_TE3
{col 23}TH2_TE4 TH3_TE1 TH3_TE2 TH3_TE3 TH3_TE4 TH4_TE1 TH4_TE2
{col 23}TH4_TE3
Dropped collinear:{col 23}mv_thirties mv_fourties mv_fifties mv_sixties
{col 23}mv_seventies mv_income2quartile mv_income3quartile
{col 23}mv_income4quartile mv_female mv_college
{col 23}mv_christiannotcatholic mv_catholic mv_fulltimeworker
{col 23}mv_unemployed mv_outofLF mv_black mv_latino mv_asian
{col 23}mv_bluecollar mv_serviceworker 4.country 7.country
{col 23}9.country 11.country 12.country
{hline 78}
({res}est2{txt} stored)

{com}. 
. estadd scalar fstat = e(widstat)

{txt}added scalar:
              e(fstat) =  {res}1.5781581
{txt}
{com}. estadd local CFE "X", replace

{txt}added macro:
                e(CFE) : "{res:X}"

{com}. estadd local Controls "X", replace

{txt}added macro:
           e(Controls) : "{res:X}"

{com}. estadd local IV "16 IVs"

{txt}added macro:
                 e(IV) : "{res:16 IVs}"

{com}. estadd local nothing nothing2 = " "

{txt}added macro:
            e(nothing) : "{res:nothing2 = " "}"

{com}. get_mean

{txt}added scalar:
                 e(av) =  {res}.5
{txt}
{com}.         
. /* 3. Col 3: 2 instruments */   
. eststo: ivreg2 satis_dem ///
>         (satis_head = healthc econc), small robust 
{res}
{txt}IV (2SLS) estimation
{hline 20}

Estimates efficient for homoskedasticity only
Statistics robust to heteroskedasticity

{col 55}Number of obs = {res}   22541
{txt}{col 55}F(  1, 22539) = {res}    4.68
{txt}{col 55}Prob > F      = {res}  0.0305
{txt}Total (centered) SS     = {res} 1635.679721{txt}{col 55}Centered R2   = {res}  0.3943
{txt}Total (uncentered) SS   = {res} 7268.530045{txt}{col 55}Uncentered R2 = {res}  0.8637
{txt}Residual SS             = {res} 990.7231405{txt}{col 55}Root MSE      = {res}   .2097

{txt}{hline 13}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 14}{c |}{col 26}    Robust
{col 1}   satis_dem{col 14}{c |} Coefficient{col 26}  std. err.{col 38}      t{col 46}   P>|t|{col 54}     [95% con{col 67}f. interval]
{hline 13}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 2}satis_head {c |}{col 14}{res}{space 2} .4760176{col 26}{space 2} .2199803{col 37}{space 1}    2.16{col 46}{space 3}0.030{col 54}{space 4} .0448409{col 67}{space 3} .9071942
{txt}{space 7}_cons {c |}{col 14}{res}{space 2} .2817841{col 26}{space 2} .1007952{col 37}{space 1}    2.80{col 46}{space 3}0.005{col 54}{space 4} .0842185{col 67}{space 3} .4793497
{txt}{hline 13}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{help ivreg2##idtest:Underidentification test} (Kleibergen-Paap rk LM statistic):{res}{col 71}   9.136
{txt}{col 52}Chi-sq({res}2{txt}) P-val =  {res}{col 73}0.0104
{txt}{hline 78}
{help ivreg2##widtest:Weak identification test} (Cragg-Donald Wald F statistic):{res}{col 71}   4.567
{txt}                         (Kleibergen-Paap rk Wald F statistic):{res}{col 71}   4.573
{txt}Stock-Yogo weak ID test critical values:{res}{txt}{col 42}10% maximal IV size{res}{col 73} 19.93
{txt}{col 42}15% maximal IV size{res}{col 73} 11.59
{txt}{col 42}20% maximal IV size{res}{col 73}  8.75
{txt}{col 42}25% maximal IV size{res}{col 73}  7.25
{txt}Source: Stock-Yogo (2005).  Reproduced by permission.
NB: Critical values are for Cragg-Donald F statistic and i.i.d. errors.
{hline 78}
{help ivreg2##overidtests:Hansen J statistic} (overidentification test of all instruments):{res}{col 71}   0.215
{txt}{col 52}Chi-sq({res}1{txt}) P-val =  {res}{col 73}0.6429
{txt}{hline 78}
Instrumented:{col 23}satis_head
Excluded instruments:{col 23}healthc econc
{hline 78}
({res}est3{txt} stored)

{com}. 
. estadd scalar fstat = e(widstat)

{txt}added scalar:
              e(fstat) =  {res}4.5733259
{txt}
{com}. estadd local IV "2 SumIVs"

{txt}added macro:
                 e(IV) : "{res:2 SumIVs}"

{com}. get_mean

{txt}added scalar:
                 e(av) =  {res}.5
{txt}
{com}. 
. 
. /* 3. Col 4: 2 instruments + controls */        
. eststo: ivreg2 satis_dem ///
>         (satis_head = healthc econc) $controls $mv_controls  i.country, small robust 
{txt}Warning - collinearities detected
Vars dropped:{col 21}mv_thirties mv_fourties mv_fifties mv_sixties mv_seventies
{col 21}mv_income2quartile mv_income3quartile mv_income4quartile
{col 21}mv_female mv_college mv_christiannotcatholic mv_catholic
{col 21}mv_fulltimeworker mv_unemployed mv_outofLF mv_black
{col 21}mv_latino mv_asian mv_bluecollar mv_serviceworker 4.country
{col 21}7.country 9.country 11.country 12.country
{res}
{txt}IV (2SLS) estimation
{hline 20}

Estimates efficient for homoskedasticity only
Statistics robust to heteroskedasticity

{col 55}Number of obs = {res}   22541
{txt}{col 55}F( 43, 22497) = {res}  113.88
{txt}{col 55}Prob > F      = {res}  0.0000
{txt}Total (centered) SS     = {res} 1635.679721{txt}{col 55}Centered R2   = {res}  0.4325
{txt}Total (uncentered) SS   = {res} 7268.530045{txt}{col 55}Uncentered R2 = {res}  0.8723
{txt}Residual SS             = {res}  928.300946{txt}{col 55}Root MSE      = {res}   .2031

{txt}{hline 24}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 25}{c |}{col 37}    Robust
{col 1}              satis_dem{col 25}{c |} Coefficient{col 37}  std. err.{col 49}      t{col 57}   P>|t|{col 65}     [95% con{col 78}f. interval]
{hline 24}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 13}satis_head {c |}{col 25}{res}{space 2}  .496087{col 37}{space 2} .2078651{col 48}{space 1}    2.39{col 57}{space 3}0.017{col 65}{space 4}  .088657{col 78}{space 3} .9035171
{txt}{space 15}thirties {c |}{col 25}{res}{space 2}-.0158398{col 37}{space 2} .0054245{col 48}{space 1}   -2.92{col 57}{space 3}0.004{col 65}{space 4}-.0264722{col 78}{space 3}-.0052073
{txt}{space 15}fourties {c |}{col 25}{res}{space 2}-.0126592{col 37}{space 2} .0080093{col 48}{space 1}   -1.58{col 57}{space 3}0.114{col 65}{space 4}-.0283579{col 78}{space 3} .0030396
{txt}{space 16}fifties {c |}{col 25}{res}{space 2}-.0119088{col 37}{space 2} .0085974{col 48}{space 1}   -1.39{col 57}{space 3}0.166{col 65}{space 4}-.0287603{col 78}{space 3} .0049428
{txt}{space 16}sixties {c |}{col 25}{res}{space 2} .0022187{col 37}{space 2} .0089469{col 48}{space 1}    0.25{col 57}{space 3}0.804{col 65}{space 4}-.0153178{col 78}{space 3} .0197553
{txt}{space 14}seventies {c |}{col 25}{res}{space 2} .0197554{col 37}{space 2} .0066427{col 48}{space 1}    2.97{col 57}{space 3}0.003{col 65}{space 4} .0067354{col 78}{space 3} .0327755
{txt}{space 8}income2quartile {c |}{col 25}{res}{space 2} .0197823{col 37}{space 2} .0053782{col 48}{space 1}    3.68{col 57}{space 3}0.000{col 65}{space 4} .0092407{col 78}{space 3}  .030324
{txt}{space 8}income3quartile {c |}{col 25}{res}{space 2} .0264801{col 37}{space 2} .0063178{col 48}{space 1}    4.19{col 57}{space 3}0.000{col 65}{space 4} .0140967{col 78}{space 3} .0388634
{txt}{space 8}income4quartile {c |}{col 25}{res}{space 2} .0428491{col 37}{space 2}  .009541{col 48}{space 1}    4.49{col 57}{space 3}0.000{col 65}{space 4}  .024148{col 78}{space 3} .0615501
{txt}{space 9}incomenoanswer {c |}{col 25}{res}{space 2} .0076651{col 37}{space 2} .0068807{col 48}{space 1}    1.11{col 57}{space 3}0.265{col 65}{space 4}-.0058215{col 78}{space 3} .0211517
{txt}{space 17}female {c |}{col 25}{res}{space 2}-.0088401{col 37}{space 2}  .003554{col 48}{space 1}   -2.49{col 57}{space 3}0.013{col 65}{space 4}-.0158063{col 78}{space 3}-.0018739
{txt}{space 13}highschool {c |}{col 25}{res}{space 2} .0057255{col 37}{space 2} .0049747{col 48}{space 1}    1.15{col 57}{space 3}0.250{col 65}{space 4}-.0040253{col 78}{space 3} .0154763
{txt}{space 16}college {c |}{col 25}{res}{space 2}  .027152{col 37}{space 2} .0043031{col 48}{space 1}    6.31{col 57}{space 3}0.000{col 65}{space 4} .0187176{col 78}{space 3} .0355865
{txt}{space 13}noreligion {c |}{col 25}{res}{space 2}-.0128322{col 37}{space 2} .0090112{col 48}{space 1}   -1.42{col 57}{space 3}0.154{col 65}{space 4}-.0304948{col 78}{space 3} .0048303
{txt}{space 3}christiannotcatholic {c |}{col 25}{res}{space 2} .0145184{col 37}{space 2} .0150489{col 48}{space 1}    0.96{col 57}{space 3}0.335{col 65}{space 4}-.0149784{col 78}{space 3} .0440152
{txt}{space 15}catholic {c |}{col 25}{res}{space 2} .0171212{col 37}{space 2} .0076737{col 48}{space 1}    2.23{col 57}{space 3}0.026{col 65}{space 4} .0020803{col 78}{space 3} .0321621
{txt}{space 9}fulltimeworker {c |}{col 25}{res}{space 2}-.0432011{col 37}{space 2} .0652707{col 48}{space 1}   -0.66{col 57}{space 3}0.508{col 65}{space 4}-.1711361{col 78}{space 3} .0847339
{txt}{space 9}parttimeworker {c |}{col 25}{res}{space 2}-.0168141{col 37}{space 2} .0660357{col 48}{space 1}   -0.25{col 57}{space 3}0.799{col 65}{space 4}-.1462486{col 78}{space 3} .1126205
{txt}{space 13}unemployed {c |}{col 25}{res}{space 2}-.0550024{col 37}{space 2} .0726458{col 48}{space 1}   -0.76{col 57}{space 3}0.449{col 65}{space 4}-.1973932{col 78}{space 3} .0873885
{txt}{space 11}selfemployed {c |}{col 25}{res}{space 2}-.0331072{col 37}{space 2} .0824944{col 48}{space 1}   -0.40{col 57}{space 3}0.688{col 65}{space 4}-.1948021{col 78}{space 3} .1285876
{txt}{space 16}outofLF {c |}{col 25}{res}{space 2}-.0431696{col 37}{space 2} .0677275{col 48}{space 1}   -0.64{col 57}{space 3}0.524{col 65}{space 4}-.1759202{col 78}{space 3}  .089581
{txt}{space 13}goodhealth {c |}{col 25}{res}{space 2} .0267539{col 37}{space 2}  .010778{col 48}{space 1}    2.48{col 57}{space 3}0.013{col 65}{space 4} .0056283{col 78}{space 3} .0478795
{txt}{space 18}white {c |}{col 25}{res}{space 2}-.0065284{col 37}{space 2} .0447919{col 48}{space 1}   -0.15{col 57}{space 3}0.884{col 65}{space 4}-.0943236{col 78}{space 3} .0812668
{txt}{space 18}black {c |}{col 25}{res}{space 2} .0673193{col 37}{space 2} .0384033{col 48}{space 1}    1.75{col 57}{space 3}0.080{col 65}{space 4} -.007954{col 78}{space 3} .1425925
{txt}{space 17}latino {c |}{col 25}{res}{space 2} .0558894{col 37}{space 2} .0408657{col 48}{space 1}    1.37{col 57}{space 3}0.171{col 65}{space 4}-.0242102{col 78}{space 3} .1359891
{txt}{space 18}asian {c |}{col 25}{res}{space 2} .0367922{col 37}{space 2} .0425538{col 48}{space 1}    0.86{col 57}{space 3}0.387{col 65}{space 4}-.0466163{col 78}{space 3} .1202006
{txt}{space 12}whitecollar {c |}{col 25}{res}{space 2} .0010727{col 37}{space 2} .0080935{col 48}{space 1}    0.13{col 57}{space 3}0.895{col 65}{space 4}-.0147912{col 78}{space 3} .0169366
{txt}{space 13}bluecollar {c |}{col 25}{res}{space 2}-.0108719{col 37}{space 2} .0089071{col 48}{space 1}   -1.22{col 57}{space 3}0.222{col 65}{space 4}-.0283304{col 78}{space 3} .0065866
{txt}{space 10}serviceworker {c |}{col 25}{res}{space 2}-.0107552{col 37}{space 2}  .006165{col 48}{space 1}   -1.74{col 57}{space 3}0.081{col 65}{space 4} -.022839{col 78}{space 3} .0013287
{txt}{space 12}mv_thirties {c |}{col 25}{res}{space 2}        0{col 37}{txt}  (omitted)
{space 12}mv_fourties {c |}{col 25}{res}{space 2}        0{col 37}{txt}  (omitted)
{space 13}mv_fifties {c |}{col 25}{res}{space 2}        0{col 37}{txt}  (omitted)
{space 13}mv_sixties {c |}{col 25}{res}{space 2}        0{col 37}{txt}  (omitted)
{space 11}mv_seventies {c |}{col 25}{res}{space 2}        0{col 37}{txt}  (omitted)
{space 5}mv_income2quartile {c |}{col 25}{res}{space 2}        0{col 37}{txt}  (omitted)
{space 5}mv_income3quartile {c |}{col 25}{res}{space 2}        0{col 37}{txt}  (omitted)
{space 5}mv_income4quartile {c |}{col 25}{res}{space 2}        0{col 37}{txt}  (omitted)
{space 6}mv_incomenoanswer {c |}{col 25}{res}{space 2} .0267268{col 37}{space 2}  .017581{col 48}{space 1}    1.52{col 57}{space 3}0.128{col 65}{space 4}-.0077332{col 78}{space 3} .0611867
{txt}{space 14}mv_female {c |}{col 25}{res}{space 2}        0{col 37}{txt}  (omitted)
{space 10}mv_highschool {c |}{col 25}{res}{space 2}  .026871{col 37}{space 2} .0108319{col 48}{space 1}    2.48{col 57}{space 3}0.013{col 65}{space 4} .0056397{col 78}{space 3} .0481022
{txt}{space 13}mv_college {c |}{col 25}{res}{space 2}        0{col 37}{txt}  (omitted)
{space 10}mv_noreligion {c |}{col 25}{res}{space 2} .0006883{col 37}{space 2} .0232006{col 48}{space 1}    0.03{col 57}{space 3}0.976{col 65}{space 4}-.0447865{col 78}{space 3} .0461632
{txt}mv_christiannotcatholic {c |}{col 25}{res}{space 2}        0{col 37}{txt}  (omitted)
{space 12}mv_catholic {c |}{col 25}{res}{space 2}        0{col 37}{txt}  (omitted)
{space 6}mv_fulltimeworker {c |}{col 25}{res}{space 2}        0{col 37}{txt}  (omitted)
{space 6}mv_parttimeworker {c |}{col 25}{res}{space 2}-.0251685{col 37}{space 2} .0617571{col 48}{space 1}   -0.41{col 57}{space 3}0.684{col 65}{space 4}-.1462167{col 78}{space 3} .0958798
{txt}{space 10}mv_unemployed {c |}{col 25}{res}{space 2}        0{col 37}{txt}  (omitted)
{space 8}mv_selfemployed {c |}{col 25}{res}{space 2} .0403816{col 37}{space 2} .0635068{col 48}{space 1}    0.64{col 57}{space 3}0.525{col 65}{space 4}-.0840962{col 78}{space 3} .1648594
{txt}{space 13}mv_outofLF {c |}{col 25}{res}{space 2}        0{col 37}{txt}  (omitted)
{space 10}mv_goodhealth {c |}{col 25}{res}{space 2}  .124172{col 37}{space 2} .0810911{col 48}{space 1}    1.53{col 57}{space 3}0.126{col 65}{space 4}-.0347721{col 78}{space 3} .2831162
{txt}{space 15}mv_white {c |}{col 25}{res}{space 2} .0427593{col 37}{space 2} .0867758{col 48}{space 1}    0.49{col 57}{space 3}0.622{col 65}{space 4}-.1273272{col 78}{space 3} .2128458
{txt}{space 15}mv_black {c |}{col 25}{res}{space 2}        0{col 37}{txt}  (omitted)
{space 14}mv_latino {c |}{col 25}{res}{space 2}        0{col 37}{txt}  (omitted)
{space 15}mv_asian {c |}{col 25}{res}{space 2}        0{col 37}{txt}  (omitted)
{space 9}mv_whitecollar {c |}{col 25}{res}{space 2}-.0140009{col 37}{space 2} .0647336{col 48}{space 1}   -0.22{col 57}{space 3}0.829{col 65}{space 4}-.1408833{col 78}{space 3} .1128814
{txt}{space 10}mv_bluecollar {c |}{col 25}{res}{space 2}        0{col 37}{txt}  (omitted)
{space 7}mv_serviceworker {c |}{col 25}{res}{space 2}        0{col 37}{txt}  (omitted)
{space 23} {c |}
{space 16}country {c |}
{space 15}Austria  {c |}{col 25}{res}{space 2} .0579676{col 37}{space 2} .1139247{col 48}{space 1}    0.51{col 57}{space 3}0.611{col 65}{space 4}-.1653327{col 78}{space 3} .2812679
{txt}{space 16}Brazil  {c |}{col 25}{res}{space 2}-.0737323{col 37}{space 2} .0150607{col 48}{space 1}   -4.90{col 57}{space 3}0.000{col 65}{space 4}-.1032523{col 78}{space 3}-.0442124
{txt}{space 16}France  {c |}{col 25}{res}{space 2}        0{col 37}{txt}  (omitted)
{space 15}Germany  {c |}{col 25}{res}{space 2} .0380245{col 37}{space 2} .1215613{col 48}{space 1}    0.31{col 57}{space 3}0.754{col 65}{space 4}-.2002442{col 78}{space 3} .2762932
{txt}{space 17}Italy  {c |}{col 25}{res}{space 2}-.0552736{col 37}{space 2} .1110989{col 48}{space 1}   -0.50{col 57}{space 3}0.619{col 65}{space 4}-.2730351{col 78}{space 3} .1624879
{txt}{space 11}New_Zealand  {c |}{col 25}{res}{space 2}        0{col 37}{txt}  (omitted)
{space 16}Poland  {c |}{col 25}{res}{space 2}-.0959585{col 37}{space 2} .0155559{col 48}{space 1}   -6.17{col 57}{space 3}0.000{col 65}{space 4} -.126449{col 78}{space 3}-.0654679
{txt}{space 17}Spain  {c |}{col 25}{res}{space 2}        0{col 37}{txt}  (omitted)
{space 16}Sweden  {c |}{col 25}{res}{space 2} .0644192{col 37}{space 2} .0185819{col 48}{space 1}    3.47{col 57}{space 3}0.001{col 65}{space 4} .0279973{col 78}{space 3} .1008411
{txt}{space 20}UK  {c |}{col 25}{res}{space 2}        0{col 37}{txt}  (omitted)
{space 20}US  {c |}{col 25}{res}{space 2}        0{col 37}{txt}  (omitted)
{space 23} {c |}
{space 18}_cons {c |}{col 25}{res}{space 2}  .205521{col 37}{space 2} .0385193{col 48}{space 1}    5.34{col 57}{space 3}0.000{col 65}{space 4} .1300205{col 78}{space 3} .2810216
{txt}{hline 24}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{help ivreg2##idtest:Underidentification test} (Kleibergen-Paap rk LM statistic):{res}{col 71}  10.904
{txt}{col 52}Chi-sq({res}2{txt}) P-val =  {res}{col 73}0.0043
{txt}{hline 78}
{help ivreg2##widtest:Weak identification test} (Cragg-Donald Wald F statistic):{res}{col 71}   5.432
{txt}                         (Kleibergen-Paap rk Wald F statistic):{res}{col 71}   5.449
{txt}Stock-Yogo weak ID test critical values:{res}{txt}{col 42}10% maximal IV size{res}{col 73} 19.93
{txt}{col 42}15% maximal IV size{res}{col 73} 11.59
{txt}{col 42}20% maximal IV size{res}{col 73}  8.75
{txt}{col 42}25% maximal IV size{res}{col 73}  7.25
{txt}Source: Stock-Yogo (2005).  Reproduced by permission.
NB: Critical values are for Cragg-Donald F statistic and i.i.d. errors.
{hline 78}
{help ivreg2##overidtests:Hansen J statistic} (overidentification test of all instruments):{res}{col 71}   0.436
{txt}{col 52}Chi-sq({res}1{txt}) P-val =  {res}{col 73}0.5089
{txt}{hline 78}
Instrumented:{col 23}satis_head
Included instruments:{col 23}thirties fourties fifties sixties seventies
{col 23}income2quartile income3quartile income4quartile
{col 23}incomenoanswer female highschool college noreligion
{col 23}christiannotcatholic catholic fulltimeworker
{col 23}parttimeworker unemployed selfemployed outofLF goodhealth
{col 23}white black latino asian whitecollar bluecollar
{col 23}serviceworker mv_incomenoanswer mv_highschool
{col 23}mv_noreligion mv_parttimeworker mv_selfemployed
{col 23}mv_goodhealth mv_white mv_whitecollar 2.country 3.country
{col 23}5.country 6.country 8.country 10.country
Excluded instruments:{col 23}healthc econc
Dropped collinear:{col 23}mv_thirties mv_fourties mv_fifties mv_sixties
{col 23}mv_seventies mv_income2quartile mv_income3quartile
{col 23}mv_income4quartile mv_female mv_college
{col 23}mv_christiannotcatholic mv_catholic mv_fulltimeworker
{col 23}mv_unemployed mv_outofLF mv_black mv_latino mv_asian
{col 23}mv_bluecollar mv_serviceworker 4.country 7.country
{col 23}9.country 11.country 12.country
{hline 78}
({res}est4{txt} stored)

{com}. 
. estadd scalar fstat = e(widstat)

{txt}added scalar:
              e(fstat) =  {res}5.4492125
{txt}
{com}. estadd local IV "2 SumIVs"

{txt}added macro:
                 e(IV) : "{res:2 SumIVs}"

{com}. estadd local Controls "X", replace

{txt}added macro:
           e(Controls) : "{res:X}"

{com}. estadd local CFE "X", replace

{txt}added macro:
                e(CFE) : "{res:X}"

{com}. get_mean

{txt}added scalar:
                 e(av) =  {res}.5
{txt}
{com}. 
. 
. /* 3. Col 5: 1 instrument */    
. eststo: ivreg2 satis_dem ///
>         (satis_head = treatc), small robust 
{res}
{txt}IV (2SLS) estimation
{hline 20}

Estimates efficient for homoskedasticity only
Statistics robust to heteroskedasticity

{col 55}Number of obs = {res}   22541
{txt}{col 55}F(  1, 22539) = {res}    3.91
{txt}{col 55}Prob > F      = {res}  0.0481
{txt}Total (centered) SS     = {res} 1635.679721{txt}{col 55}Centered R2   = {res}  0.3887
{txt}Total (uncentered) SS   = {res} 7268.530045{txt}{col 55}Uncentered R2 = {res}  0.8624
{txt}Residual SS             = {res} 999.8930152{txt}{col 55}Root MSE      = {res}   .2106

{txt}{hline 13}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 14}{c |}{col 26}    Robust
{col 1}   satis_dem{col 14}{c |} Coefficient{col 26}  std. err.{col 38}      t{col 46}   P>|t|{col 54}     [95% con{col 67}f. interval]
{hline 13}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 2}satis_head {c |}{col 14}{res}{space 2} .4501237{col 26}{space 2}  .227723{col 37}{space 1}    1.98{col 46}{space 3}0.048{col 54}{space 4} .0037709{col 67}{space 3} .8964765
{txt}{space 7}_cons {c |}{col 14}{res}{space 2} .2936486{col 26}{space 2} .1043448{col 37}{space 1}    2.81{col 46}{space 3}0.005{col 54}{space 4} .0891256{col 67}{space 3} .4981716
{txt}{hline 13}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{help ivreg2##idtest:Underidentification test} (Kleibergen-Paap rk LM statistic):{res}{col 71}   8.585
{txt}{col 52}Chi-sq({res}1{txt}) P-val =  {res}{col 73}0.0034
{txt}{hline 78}
{help ivreg2##widtest:Weak identification test} (Cragg-Donald Wald F statistic):{res}{col 71}   8.590
{txt}                         (Kleibergen-Paap rk Wald F statistic):{res}{col 71}   8.595
{txt}Stock-Yogo weak ID test critical values:{res}{txt}{col 42}10% maximal IV size{res}{col 73} 16.38
{txt}{col 42}15% maximal IV size{res}{col 73}  8.96
{txt}{col 42}20% maximal IV size{res}{col 73}  6.66
{txt}{col 42}25% maximal IV size{res}{col 73}  5.53
{txt}Source: Stock-Yogo (2005).  Reproduced by permission.
NB: Critical values are for Cragg-Donald F statistic and i.i.d. errors.
{hline 78}
{help ivreg2##overidtests:Hansen J statistic} (overidentification test of all instruments):{res}{col 71}   0.000
{txt}{col 50}(equation exactly identified)
{hline 78}
Instrumented:{col 23}satis_head
Excluded instruments:{col 23}treatc
{hline 78}
({res}est5{txt} stored)

{com}. 
. estadd scalar fstat = e(widstat)

{txt}added scalar:
              e(fstat) =  {res}8.5952917
{txt}
{com}. estadd local IV "SumIV"

{txt}added macro:
                 e(IV) : "{res:SumIV}"

{com}. estadd local nothing nothing2 = " "

{txt}added macro:
            e(nothing) : "{res:nothing2 = " "}"

{com}. get_mean

{txt}added scalar:
                 e(av) =  {res}.5
{txt}
{com}. 
. /* 3. Col 6: 1 instrument + controls */                                                                         
. eststo: ivreg2 satis_dem ///
>         (satis_head = treatc) $controls $mv_controls  i.country, small robust 
{txt}Warning - collinearities detected
Vars dropped:{col 21}mv_thirties mv_fourties mv_fifties mv_sixties mv_seventies
{col 21}mv_income2quartile mv_income3quartile mv_income4quartile
{col 21}mv_female mv_college mv_christiannotcatholic mv_catholic
{col 21}mv_fulltimeworker mv_unemployed mv_outofLF mv_black
{col 21}mv_latino mv_asian mv_bluecollar mv_serviceworker 4.country
{col 21}7.country 9.country 11.country 12.country
{res}
{txt}IV (2SLS) estimation
{hline 20}

Estimates efficient for homoskedasticity only
Statistics robust to heteroskedasticity

{col 55}Number of obs = {res}   22541
{txt}{col 55}F( 43, 22497) = {res}  112.74
{txt}{col 55}Prob > F      = {res}  0.0000
{txt}Total (centered) SS     = {res} 1635.679721{txt}{col 55}Centered R2   = {res}  0.4289
{txt}Total (uncentered) SS   = {res} 7268.530045{txt}{col 55}Uncentered R2 = {res}  0.8715
{txt}Residual SS             = {res} 934.1839376{txt}{col 55}Root MSE      = {res}   .2038

{txt}{hline 24}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 25}{c |}{col 37}    Robust
{col 1}              satis_dem{col 25}{c |} Coefficient{col 37}  std. err.{col 49}      t{col 57}   P>|t|{col 65}     [95% con{col 78}f. interval]
{hline 24}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 13}satis_head {c |}{col 25}{res}{space 2} .4602277{col 37}{space 2} .2152753{col 48}{space 1}    2.14{col 57}{space 3}0.033{col 65}{space 4} .0382732{col 78}{space 3} .8821822
{txt}{space 15}thirties {c |}{col 25}{res}{space 2}-.0163162{col 37}{space 2} .0054864{col 48}{space 1}   -2.97{col 57}{space 3}0.003{col 65}{space 4}-.0270699{col 78}{space 3}-.0055625
{txt}{space 15}fourties {c |}{col 25}{res}{space 2}-.0137927{col 37}{space 2} .0082023{col 48}{space 1}   -1.68{col 57}{space 3}0.093{col 65}{space 4}-.0298697{col 78}{space 3} .0022843
{txt}{space 16}fifties {c |}{col 25}{res}{space 2}-.0131227{col 37}{space 2} .0088123{col 48}{space 1}   -1.49{col 57}{space 3}0.136{col 65}{space 4}-.0303954{col 78}{space 3} .0041501
{txt}{space 16}sixties {c |}{col 25}{res}{space 2}  .000937{col 37}{space 2} .0091638{col 48}{space 1}    0.10{col 57}{space 3}0.919{col 65}{space 4}-.0170248{col 78}{space 3} .0188988
{txt}{space 14}seventies {c |}{col 25}{res}{space 2} .0192585{col 37}{space 2} .0066764{col 48}{space 1}    2.88{col 57}{space 3}0.004{col 65}{space 4} .0061723{col 78}{space 3} .0323446
{txt}{space 8}income2quartile {c |}{col 25}{res}{space 2} .0204001{col 37}{space 2} .0054765{col 48}{space 1}    3.73{col 57}{space 3}0.000{col 65}{space 4} .0096657{col 78}{space 3} .0311345
{txt}{space 8}income3quartile {c |}{col 25}{res}{space 2} .0272993{col 37}{space 2} .0064653{col 48}{space 1}    4.22{col 57}{space 3}0.000{col 65}{space 4} .0146268{col 78}{space 3} .0399717
{txt}{space 8}income4quartile {c |}{col 25}{res}{space 2} .0443291{col 37}{space 2} .0098278{col 48}{space 1}    4.51{col 57}{space 3}0.000{col 65}{space 4}  .025066{col 78}{space 3} .0635922
{txt}{space 9}incomenoanswer {c |}{col 25}{res}{space 2}  .007305{col 37}{space 2} .0069159{col 48}{space 1}    1.06{col 57}{space 3}0.291{col 65}{space 4}-.0062506{col 78}{space 3} .0208606
{txt}{space 17}female {c |}{col 25}{res}{space 2}-.0084555{col 37}{space 2} .0036132{col 48}{space 1}   -2.34{col 57}{space 3}0.019{col 65}{space 4}-.0155376{col 78}{space 3}-.0013734
{txt}{space 13}highschool {c |}{col 25}{res}{space 2} .0060547{col 37}{space 2} .0050124{col 48}{space 1}    1.21{col 57}{space 3}0.227{col 65}{space 4}  -.00377{col 78}{space 3} .0158794
{txt}{space 16}college {c |}{col 25}{res}{space 2} .0272436{col 37}{space 2} .0043341{col 48}{space 1}    6.29{col 57}{space 3}0.000{col 65}{space 4} .0187485{col 78}{space 3} .0357387
{txt}{space 13}noreligion {c |}{col 25}{res}{space 2}-.0139496{col 37}{space 2} .0091656{col 48}{space 1}   -1.52{col 57}{space 3}0.128{col 65}{space 4}-.0319148{col 78}{space 3} .0040157
{txt}{space 3}christiannotcatholic {c |}{col 25}{res}{space 2} .0168088{col 37}{space 2} .0154852{col 48}{space 1}    1.09{col 57}{space 3}0.278{col 65}{space 4}-.0135432{col 78}{space 3} .0471609
{txt}{space 15}catholic {c |}{col 25}{res}{space 2} .0178882{col 37}{space 2} .0077855{col 48}{space 1}    2.30{col 57}{space 3}0.022{col 65}{space 4}  .002628{col 78}{space 3} .0331484
{txt}{space 9}fulltimeworker {c |}{col 25}{res}{space 2}-.0544075{col 37}{space 2} .0675621{col 48}{space 1}   -0.81{col 57}{space 3}0.421{col 65}{space 4}-.1868339{col 78}{space 3} .0780189
{txt}{space 9}parttimeworker {c |}{col 25}{res}{space 2}-.0277896{col 37}{space 2} .0681438{col 48}{space 1}   -0.41{col 57}{space 3}0.683{col 65}{space 4}-.1613561{col 78}{space 3} .1057769
{txt}{space 13}unemployed {c |}{col 25}{res}{space 2}-.0674139{col 37}{space 2} .0751475{col 48}{space 1}   -0.90{col 57}{space 3}0.370{col 65}{space 4}-.2147082{col 78}{space 3} .0798804
{txt}{space 11}selfemployed {c |}{col 25}{res}{space 2}-.0463537{col 37}{space 2} .0850118{col 48}{space 1}   -0.55{col 57}{space 3}0.586{col 65}{space 4}-.2129827{col 78}{space 3} .1202753
{txt}{space 16}outofLF {c |}{col 25}{res}{space 2}-.0548057{col 37}{space 2} .0701063{col 48}{space 1}   -0.78{col 57}{space 3}0.434{col 65}{space 4}-.1922189{col 78}{space 3} .0826075
{txt}{space 13}goodhealth {c |}{col 25}{res}{space 2} .0285493{col 37}{space 2} .0111475{col 48}{space 1}    2.56{col 57}{space 3}0.010{col 65}{space 4} .0066995{col 78}{space 3} .0503991
{txt}{space 18}white {c |}{col 25}{res}{space 2}-.0018041{col 37}{space 2} .0450602{col 48}{space 1}   -0.04{col 57}{space 3}0.968{col 65}{space 4}-.0901251{col 78}{space 3}  .086517
{txt}{space 18}black {c |}{col 25}{res}{space 2} .0677086{col 37}{space 2} .0380587{col 48}{space 1}    1.78{col 57}{space 3}0.075{col 65}{space 4} -.006889{col 78}{space 3} .1423063
{txt}{space 17}latino {c |}{col 25}{res}{space 2} .0571548{col 37}{space 2}  .040592{col 48}{space 1}    1.41{col 57}{space 3}0.159{col 65}{space 4}-.0224084{col 78}{space 3}  .136718
{txt}{space 18}asian {c |}{col 25}{res}{space 2} .0393975{col 37}{space 2} .0423559{col 48}{space 1}    0.93{col 57}{space 3}0.352{col 65}{space 4}-.0436231{col 78}{space 3} .1224181
{txt}{space 12}whitecollar {c |}{col 25}{res}{space 2} .0004524{col 37}{space 2} .0081698{col 48}{space 1}    0.06{col 57}{space 3}0.956{col 65}{space 4}-.0155609{col 78}{space 3} .0164657
{txt}{space 13}bluecollar {c |}{col 25}{res}{space 2}-.0117158{col 37}{space 2} .0090323{col 48}{space 1}   -1.30{col 57}{space 3}0.195{col 65}{space 4}-.0294197{col 78}{space 3} .0059881
{txt}{space 10}serviceworker {c |}{col 25}{res}{space 2}-.0111075{col 37}{space 2} .0062126{col 48}{space 1}   -1.79{col 57}{space 3}0.074{col 65}{space 4}-.0232847{col 78}{space 3} .0010697
{txt}{space 12}mv_thirties {c |}{col 25}{res}{space 2}        0{col 37}{txt}  (omitted)
{space 12}mv_fourties {c |}{col 25}{res}{space 2}        0{col 37}{txt}  (omitted)
{space 13}mv_fifties {c |}{col 25}{res}{space 2}        0{col 37}{txt}  (omitted)
{space 13}mv_sixties {c |}{col 25}{res}{space 2}        0{col 37}{txt}  (omitted)
{space 11}mv_seventies {c |}{col 25}{res}{space 2}        0{col 37}{txt}  (omitted)
{space 5}mv_income2quartile {c |}{col 25}{res}{space 2}        0{col 37}{txt}  (omitted)
{space 5}mv_income3quartile {c |}{col 25}{res}{space 2}        0{col 37}{txt}  (omitted)
{space 5}mv_income4quartile {c |}{col 25}{res}{space 2}        0{col 37}{txt}  (omitted)
{space 6}mv_incomenoanswer {c |}{col 25}{res}{space 2} .0240656{col 37}{space 2} .0180579{col 48}{space 1}    1.33{col 57}{space 3}0.183{col 65}{space 4}-.0113292{col 78}{space 3} .0594604
{txt}{space 14}mv_female {c |}{col 25}{res}{space 2}        0{col 37}{txt}  (omitted)
{space 10}mv_highschool {c |}{col 25}{res}{space 2} .0264803{col 37}{space 2} .0109552{col 48}{space 1}    2.42{col 57}{space 3}0.016{col 65}{space 4} .0050074{col 78}{space 3} .0479533
{txt}{space 13}mv_college {c |}{col 25}{res}{space 2}        0{col 37}{txt}  (omitted)
{space 10}mv_noreligion {c |}{col 25}{res}{space 2} .0008534{col 37}{space 2} .0230974{col 48}{space 1}    0.04{col 57}{space 3}0.971{col 65}{space 4}-.0444191{col 78}{space 3}  .046126
{txt}mv_christiannotcatholic {c |}{col 25}{res}{space 2}        0{col 37}{txt}  (omitted)
{space 12}mv_catholic {c |}{col 25}{res}{space 2}        0{col 37}{txt}  (omitted)
{space 6}mv_fulltimeworker {c |}{col 25}{res}{space 2}        0{col 37}{txt}  (omitted)
{space 6}mv_parttimeworker {c |}{col 25}{res}{space 2}-.0355326{col 37}{space 2} .0637749{col 48}{space 1}   -0.56{col 57}{space 3}0.577{col 65}{space 4}-.1605359{col 78}{space 3} .0894707
{txt}{space 10}mv_unemployed {c |}{col 25}{res}{space 2}        0{col 37}{txt}  (omitted)
{space 8}mv_selfemployed {c |}{col 25}{res}{space 2} .0510191{col 37}{space 2} .0655577{col 48}{space 1}    0.78{col 57}{space 3}0.436{col 65}{space 4}-.0774786{col 78}{space 3} .1795169
{txt}{space 13}mv_outofLF {c |}{col 25}{res}{space 2}        0{col 37}{txt}  (omitted)
{space 10}mv_goodhealth {c |}{col 25}{res}{space 2} .1305382{col 37}{space 2} .0823984{col 48}{space 1}    1.58{col 57}{space 3}0.113{col 65}{space 4}-.0309684{col 78}{space 3} .2920447
{txt}{space 15}mv_white {c |}{col 25}{res}{space 2} .0564167{col 37}{space 2}  .089138{col 48}{space 1}    0.63{col 57}{space 3}0.527{col 65}{space 4}-.1182999{col 78}{space 3} .2311333
{txt}{space 15}mv_black {c |}{col 25}{res}{space 2}        0{col 37}{txt}  (omitted)
{space 14}mv_latino {c |}{col 25}{res}{space 2}        0{col 37}{txt}  (omitted)
{space 15}mv_asian {c |}{col 25}{res}{space 2}        0{col 37}{txt}  (omitted)
{space 9}mv_whitecollar {c |}{col 25}{res}{space 2} -.003113{col 37}{space 2} .0669482{col 48}{space 1}   -0.05{col 57}{space 3}0.963{col 65}{space 4}-.1343362{col 78}{space 3} .1281102
{txt}{space 10}mv_bluecollar {c |}{col 25}{res}{space 2}        0{col 37}{txt}  (omitted)
{space 7}mv_serviceworker {c |}{col 25}{res}{space 2}        0{col 37}{txt}  (omitted)
{space 23} {c |}
{space 16}country {c |}
{space 15}Austria  {c |}{col 25}{res}{space 2} .0764516{col 37}{space 2} .1173835{col 48}{space 1}    0.65{col 57}{space 3}0.515{col 65}{space 4}-.1536282{col 78}{space 3} .3065314
{txt}{space 16}Brazil  {c |}{col 25}{res}{space 2}-.0743078{col 37}{space 2} .0151116{col 48}{space 1}   -4.92{col 57}{space 3}0.000{col 65}{space 4}-.1039275{col 78}{space 3} -.044688
{txt}{space 16}France  {c |}{col 25}{res}{space 2}        0{col 37}{txt}  (omitted)
{space 15}Germany  {c |}{col 25}{res}{space 2} .0579013{col 37}{space 2} .1253163{col 48}{space 1}    0.46{col 57}{space 3}0.644{col 65}{space 4}-.1877273{col 78}{space 3} .3035299
{txt}{space 17}Italy  {c |}{col 25}{res}{space 2}-.0377034{col 37}{space 2} .1142492{col 48}{space 1}   -0.33{col 57}{space 3}0.741{col 65}{space 4}-.2616398{col 78}{space 3} .1862329
{txt}{space 11}New_Zealand  {c |}{col 25}{res}{space 2}        0{col 37}{txt}  (omitted)
{space 16}Poland  {c |}{col 25}{res}{space 2}-.0971901{col 37}{space 2}  .015801{col 48}{space 1}   -6.15{col 57}{space 3}0.000{col 65}{space 4}-.1281612{col 78}{space 3} -.066219
{txt}{space 17}Spain  {c |}{col 25}{res}{space 2}        0{col 37}{txt}  (omitted)
{space 16}Sweden  {c |}{col 25}{res}{space 2}  .066522{col 37}{space 2} .0189536{col 48}{space 1}    3.51{col 57}{space 3}0.000{col 65}{space 4} .0293716{col 78}{space 3} .1036724
{txt}{space 20}UK  {c |}{col 25}{res}{space 2}        0{col 37}{txt}  (omitted)
{space 20}US  {c |}{col 25}{res}{space 2}        0{col 37}{txt}  (omitted)
{space 23} {c |}
{space 18}_cons {c |}{col 25}{res}{space 2} .2052342{col 37}{space 2} .0382525{col 48}{space 1}    5.37{col 57}{space 3}0.000{col 65}{space 4} .1302567{col 78}{space 3} .2802118
{txt}{hline 24}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{help ivreg2##idtest:Underidentification test} (Kleibergen-Paap rk LM statistic):{res}{col 71}  10.212
{txt}{col 52}Chi-sq({res}1{txt}) P-val =  {res}{col 73}0.0014
{txt}{hline 78}
{help ivreg2##widtest:Weak identification test} (Cragg-Donald Wald F statistic):{res}{col 71}  10.181
{txt}                         (Kleibergen-Paap rk Wald F statistic):{res}{col 71}  10.208
{txt}Stock-Yogo weak ID test critical values:{res}{txt}{col 42}10% maximal IV size{res}{col 73} 16.38
{txt}{col 42}15% maximal IV size{res}{col 73}  8.96
{txt}{col 42}20% maximal IV size{res}{col 73}  6.66
{txt}{col 42}25% maximal IV size{res}{col 73}  5.53
{txt}Source: Stock-Yogo (2005).  Reproduced by permission.
NB: Critical values are for Cragg-Donald F statistic and i.i.d. errors.
{hline 78}
{help ivreg2##overidtests:Hansen J statistic} (overidentification test of all instruments):{res}{col 71}   0.000
{txt}{col 50}(equation exactly identified)
{hline 78}
Instrumented:{col 23}satis_head
Included instruments:{col 23}thirties fourties fifties sixties seventies
{col 23}income2quartile income3quartile income4quartile
{col 23}incomenoanswer female highschool college noreligion
{col 23}christiannotcatholic catholic fulltimeworker
{col 23}parttimeworker unemployed selfemployed outofLF goodhealth
{col 23}white black latino asian whitecollar bluecollar
{col 23}serviceworker mv_incomenoanswer mv_highschool
{col 23}mv_noreligion mv_parttimeworker mv_selfemployed
{col 23}mv_goodhealth mv_white mv_whitecollar 2.country 3.country
{col 23}5.country 6.country 8.country 10.country
Excluded instruments:{col 23}treatc
Dropped collinear:{col 23}mv_thirties mv_fourties mv_fifties mv_sixties
{col 23}mv_seventies mv_income2quartile mv_income3quartile
{col 23}mv_income4quartile mv_female mv_college
{col 23}mv_christiannotcatholic mv_catholic mv_fulltimeworker
{col 23}mv_unemployed mv_outofLF mv_black mv_latino mv_asian
{col 23}mv_bluecollar mv_serviceworker 4.country 7.country
{col 23}9.country 11.country 12.country
{hline 78}
({res}est6{txt} stored)

{com}.         
. estadd scalar fstat = e(widstat)

{txt}added scalar:
              e(fstat) =  {res}10.207647
{txt}
{com}. estadd local IV "SumIV"

{txt}added macro:
                 e(IV) : "{res:SumIV}"

{com}. estadd local nothing nothing2 = " "

{txt}added macro:
            e(nothing) : "{res:nothing2 = " "}"

{com}. estadd local Controls "X", replace

{txt}added macro:
           e(Controls) : "{res:X}"

{com}. estadd local CFE "X", replace

{txt}added macro:
                e(CFE) : "{res:X}"

{com}. get_mean

{txt}added scalar:
                 e(av) =  {res}.5
{txt}
{com}. 
. esttab using "3_output/1_main_paper/Tables/Table3.tex", depvar keep(satis_head) ///
>         order(eval_eco eval_sant serious_h_csqc serious_e_csqc) label replace wrap ///
>         cells(b(star fmt(%15.3fc)) se(par fmt(%15.3fc))) interaction(" $/times$ ") ///
>         star(* 0.10 ** 0.05 *** 0.01) ///
>         stats(Controls CFE N av IV fstat , layout(@ @ @ @ @ @ ) fmt(%15s %15s %15.0fc %9.3f %9.3f %9.3f ) ///
>         labels("Individual controls" "Country FE" Observations "Outcome mean" "Instruments" "F-statistic" )) ///
>         collabels(none) mlabels(none) ///
>         mgroups("Satisfaction with democracy" , pattern(1 0 0 0) ///
>         prefix(\multicolumn{c -(}@span{c )-}{c -(}c{c )-}{c -(}) suffix({c )-}) span erepeat(\cmidrule(lr){c -(}@span{c )-}))
{res}{txt}(output written to {browse  `"3_output/1_main_paper/Tables/Table3.tex"'})

{com}.                         
.                 
. **# Table 4: Impact on support for democratic ideals 
. 
. eststo clear
{txt}
{com}. /* 4. Col 1: Strong leader with 16 instruments */       
. * to get the Cragg-Donald statistic: 
. eststo: ivreg2 strong_leader ///
>         (satis_head = TH1_TE1 TH1_TE2 TH1_TE3 TH1_TE4 TH2_TE1 TH2_TE2 TH2_TE3 TH2_TE4 TH3_TE1 TH3_TE2 TH3_TE3 TH3_TE4 TH4_TE1 TH4_TE2 TH4_TE3) $controls $mv_controls  i.country, small robust 
{txt}Warning - collinearities detected
Vars dropped:{col 21}mv_thirties mv_fourties mv_fifties mv_sixties mv_seventies
{col 21}mv_income2quartile mv_income3quartile mv_income4quartile
{col 21}mv_female mv_college mv_christiannotcatholic mv_catholic
{col 21}mv_fulltimeworker mv_unemployed mv_outofLF mv_black
{col 21}mv_latino mv_asian mv_bluecollar mv_serviceworker 4.country
{col 21}7.country 9.country 11.country 12.country
{res}
{txt}IV (2SLS) estimation
{hline 20}

Estimates efficient for homoskedasticity only
Statistics robust to heteroskedasticity

{col 55}Number of obs = {res}   22535
{txt}{col 55}F( 43, 22491) = {res}   39.57
{txt}{col 55}Prob > F      = {res}  0.0000
{txt}Total (centered) SS     = {res} 4867.836077{txt}{col 55}Centered R2   = {res}  0.0395
{txt}Total (uncentered) SS   = {res}        7113{txt}{col 55}Uncentered R2 = {res}  0.3427
{txt}Residual SS             = {res} 4675.647176{txt}{col 55}Root MSE      = {res}   .4559

{txt}{hline 24}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 25}{c |}{col 37}    Robust
{col 1}          strong_leader{col 25}{c |} Coefficient{col 37}  std. err.{col 49}      t{col 57}   P>|t|{col 65}     [95% con{col 78}f. interval]
{hline 24}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 13}satis_head {c |}{col 25}{res}{space 2}-.1645855{col 37}{space 2} .3201513{col 48}{space 1}   -0.51{col 57}{space 3}0.607{col 65}{space 4}-.7921043{col 78}{space 3} .4629333
{txt}{space 15}thirties {c |}{col 25}{res}{space 2} .0342584{col 37}{space 2} .0121133{col 48}{space 1}    2.83{col 57}{space 3}0.005{col 65}{space 4} .0105155{col 78}{space 3} .0580012
{txt}{space 15}fourties {c |}{col 25}{res}{space 2}-.0115521{col 37}{space 2} .0149951{col 48}{space 1}   -0.77{col 57}{space 3}0.441{col 65}{space 4}-.0409436{col 78}{space 3} .0178394
{txt}{space 16}fifties {c |}{col 25}{res}{space 2}-.0517995{col 37}{space 2} .0156379{col 48}{space 1}   -3.31{col 57}{space 3}0.001{col 65}{space 4} -.082451{col 78}{space 3}-.0211481
{txt}{space 16}sixties {c |}{col 25}{res}{space 2}-.0757043{col 37}{space 2} .0159996{col 48}{space 1}   -4.73{col 57}{space 3}0.000{col 65}{space 4}-.1070646{col 78}{space 3}-.0443441
{txt}{space 14}seventies {c |}{col 25}{res}{space 2}-.0774604{col 37}{space 2} .0137502{col 48}{space 1}   -5.63{col 57}{space 3}0.000{col 65}{space 4}-.1044117{col 78}{space 3}-.0505091
{txt}{space 8}income2quartile {c |}{col 25}{res}{space 2}-.0132562{col 37}{space 2} .0106328{col 48}{space 1}   -1.25{col 57}{space 3}0.213{col 65}{space 4}-.0340972{col 78}{space 3} .0075847
{txt}{space 8}income3quartile {c |}{col 25}{res}{space 2}-.0106807{col 37}{space 2} .0118632{col 48}{space 1}   -0.90{col 57}{space 3}0.368{col 65}{space 4}-.0339333{col 78}{space 3}  .012572
{txt}{space 8}income4quartile {c |}{col 25}{res}{space 2}  -.03283{col 37}{space 2} .0163023{col 48}{space 1}   -2.01{col 57}{space 3}0.044{col 65}{space 4}-.0647836{col 78}{space 3}-.0008763
{txt}{space 9}incomenoanswer {c |}{col 25}{res}{space 2}-.0066949{col 37}{space 2} .0143495{col 48}{space 1}   -0.47{col 57}{space 3}0.641{col 65}{space 4} -.034821{col 78}{space 3} .0214311
{txt}{space 17}female {c |}{col 25}{res}{space 2}-.0197438{col 37}{space 2} .0070783{col 48}{space 1}   -2.79{col 57}{space 3}0.005{col 65}{space 4}-.0336178{col 78}{space 3}-.0058699
{txt}{space 13}highschool {c |}{col 25}{res}{space 2}-.0997878{col 37}{space 2} .0113973{col 48}{space 1}   -8.76{col 57}{space 3}0.000{col 65}{space 4}-.1221273{col 78}{space 3}-.0774482
{txt}{space 16}college {c |}{col 25}{res}{space 2}-.1637638{col 37}{space 2} .0103193{col 48}{space 1}  -15.87{col 57}{space 3}0.000{col 65}{space 4}-.1839903{col 78}{space 3}-.1435373
{txt}{space 13}noreligion {c |}{col 25}{res}{space 2}-.1454415{col 37}{space 2} .0171441{col 48}{space 1}   -8.48{col 57}{space 3}0.000{col 65}{space 4}-.1790453{col 78}{space 3}-.1118378
{txt}{space 3}christiannotcatholic {c |}{col 25}{res}{space 2}-.0621979{col 37}{space 2} .0258059{col 48}{space 1}   -2.41{col 57}{space 3}0.016{col 65}{space 4}-.1127793{col 78}{space 3}-.0116165
{txt}{space 15}catholic {c |}{col 25}{res}{space 2}-.0284061{col 37}{space 2} .0159413{col 48}{space 1}   -1.78{col 57}{space 3}0.075{col 65}{space 4}-.0596522{col 78}{space 3}   .00284
{txt}{space 9}fulltimeworker {c |}{col 25}{res}{space 2}-.1000706{col 37}{space 2} .1016038{col 48}{space 1}   -0.98{col 57}{space 3}0.325{col 65}{space 4}-.2992211{col 78}{space 3}   .09908
{txt}{space 9}parttimeworker {c |}{col 25}{res}{space 2}-.1486604{col 37}{space 2} .1051973{col 48}{space 1}   -1.41{col 57}{space 3}0.158{col 65}{space 4}-.3548543{col 78}{space 3} .0575336
{txt}{space 13}unemployed {c |}{col 25}{res}{space 2}-.1382167{col 37}{space 2} .1139606{col 48}{space 1}   -1.21{col 57}{space 3}0.225{col 65}{space 4}-.3615875{col 78}{space 3}  .085154
{txt}{space 11}selfemployed {c |}{col 25}{res}{space 2}-.0776914{col 37}{space 2} .1305399{col 48}{space 1}   -0.60{col 57}{space 3}0.552{col 65}{space 4}-.3335586{col 78}{space 3} .1781758
{txt}{space 16}outofLF {c |}{col 25}{res}{space 2}-.0885454{col 37}{space 2} .1055818{col 48}{space 1}   -0.84{col 57}{space 3}0.402{col 65}{space 4} -.295493{col 78}{space 3} .1184022
{txt}{space 13}goodhealth {c |}{col 25}{res}{space 2} .0121231{col 37}{space 2} .0173199{col 48}{space 1}    0.70{col 57}{space 3}0.484{col 65}{space 4}-.0218251{col 78}{space 3} .0460713
{txt}{space 18}white {c |}{col 25}{res}{space 2} .0660016{col 37}{space 2} .0766503{col 48}{space 1}    0.86{col 57}{space 3}0.389{col 65}{space 4}-.0842383{col 78}{space 3} .2162415
{txt}{space 18}black {c |}{col 25}{res}{space 2} .1445272{col 37}{space 2} .0721502{col 48}{space 1}    2.00{col 57}{space 3}0.045{col 65}{space 4} .0031078{col 78}{space 3} .2859467
{txt}{space 17}latino {c |}{col 25}{res}{space 2} .1824946{col 37}{space 2} .0797964{col 48}{space 1}    2.29{col 57}{space 3}0.022{col 65}{space 4} .0260881{col 78}{space 3}  .338901
{txt}{space 18}asian {c |}{col 25}{res}{space 2}  .154454{col 37}{space 2} .0823041{col 48}{space 1}    1.88{col 57}{space 3}0.061{col 65}{space 4}-.0068678{col 78}{space 3} .3157758
{txt}{space 12}whitecollar {c |}{col 25}{res}{space 2} .0013845{col 37}{space 2} .0157754{col 48}{space 1}    0.09{col 57}{space 3}0.930{col 65}{space 4}-.0295363{col 78}{space 3} .0323054
{txt}{space 13}bluecollar {c |}{col 25}{res}{space 2} .0378872{col 37}{space 2} .0177969{col 48}{space 1}    2.13{col 57}{space 3}0.033{col 65}{space 4}  .003004{col 78}{space 3} .0727704
{txt}{space 10}serviceworker {c |}{col 25}{res}{space 2}  .014245{col 37}{space 2} .0128565{col 48}{space 1}    1.11{col 57}{space 3}0.268{col 65}{space 4}-.0109546{col 78}{space 3} .0394446
{txt}{space 12}mv_thirties {c |}{col 25}{res}{space 2}        0{col 37}{txt}  (omitted)
{space 12}mv_fourties {c |}{col 25}{res}{space 2}        0{col 37}{txt}  (omitted)
{space 13}mv_fifties {c |}{col 25}{res}{space 2}        0{col 37}{txt}  (omitted)
{space 13}mv_sixties {c |}{col 25}{res}{space 2}        0{col 37}{txt}  (omitted)
{space 11}mv_seventies {c |}{col 25}{res}{space 2}        0{col 37}{txt}  (omitted)
{space 5}mv_income2quartile {c |}{col 25}{res}{space 2}        0{col 37}{txt}  (omitted)
{space 5}mv_income3quartile {c |}{col 25}{res}{space 2}        0{col 37}{txt}  (omitted)
{space 5}mv_income4quartile {c |}{col 25}{res}{space 2}        0{col 37}{txt}  (omitted)
{space 6}mv_incomenoanswer {c |}{col 25}{res}{space 2} .0828146{col 37}{space 2} .0326663{col 48}{space 1}    2.54{col 57}{space 3}0.011{col 65}{space 4} .0187864{col 78}{space 3} .1468428
{txt}{space 14}mv_female {c |}{col 25}{res}{space 2}        0{col 37}{txt}  (omitted)
{space 10}mv_highschool {c |}{col 25}{res}{space 2}-.0634007{col 37}{space 2} .0270603{col 48}{space 1}   -2.34{col 57}{space 3}0.019{col 65}{space 4}-.1164408{col 78}{space 3}-.0103607
{txt}{space 13}mv_college {c |}{col 25}{res}{space 2}        0{col 37}{txt}  (omitted)
{space 10}mv_noreligion {c |}{col 25}{res}{space 2}-.0287371{col 37}{space 2} .0465847{col 48}{space 1}   -0.62{col 57}{space 3}0.537{col 65}{space 4}-.1200463{col 78}{space 3}  .062572
{txt}mv_christiannotcatholic {c |}{col 25}{res}{space 2}        0{col 37}{txt}  (omitted)
{space 12}mv_catholic {c |}{col 25}{res}{space 2}        0{col 37}{txt}  (omitted)
{space 6}mv_fulltimeworker {c |}{col 25}{res}{space 2}        0{col 37}{txt}  (omitted)
{space 6}mv_parttimeworker {c |}{col 25}{res}{space 2}-.0931549{col 37}{space 2} .0976329{col 48}{space 1}   -0.95{col 57}{space 3}0.340{col 65}{space 4}-.2845221{col 78}{space 3} .0982123
{txt}{space 10}mv_unemployed {c |}{col 25}{res}{space 2}        0{col 37}{txt}  (omitted)
{space 8}mv_selfemployed {c |}{col 25}{res}{space 2} .1240533{col 37}{space 2} .1004352{col 48}{space 1}    1.24{col 57}{space 3}0.217{col 65}{space 4}-.0728067{col 78}{space 3} .3209133
{txt}{space 13}mv_outofLF {c |}{col 25}{res}{space 2}        0{col 37}{txt}  (omitted)
{space 10}mv_goodhealth {c |}{col 25}{res}{space 2} .2265148{col 37}{space 2}  .154464{col 48}{space 1}    1.47{col 57}{space 3}0.143{col 65}{space 4}-.0762453{col 78}{space 3} .5292749
{txt}{space 15}mv_white {c |}{col 25}{res}{space 2} .1928737{col 37}{space 2} .1377959{col 48}{space 1}    1.40{col 57}{space 3}0.162{col 65}{space 4}-.0772159{col 78}{space 3} .4629633
{txt}{space 15}mv_black {c |}{col 25}{res}{space 2}        0{col 37}{txt}  (omitted)
{space 14}mv_latino {c |}{col 25}{res}{space 2}        0{col 37}{txt}  (omitted)
{space 15}mv_asian {c |}{col 25}{res}{space 2}        0{col 37}{txt}  (omitted)
{space 9}mv_whitecollar {c |}{col 25}{res}{space 2} .2086794{col 37}{space 2} .1030995{col 48}{space 1}    2.02{col 57}{space 3}0.043{col 65}{space 4} .0065973{col 78}{space 3} .4107615
{txt}{space 10}mv_bluecollar {c |}{col 25}{res}{space 2}        0{col 37}{txt}  (omitted)
{space 7}mv_serviceworker {c |}{col 25}{res}{space 2}        0{col 37}{txt}  (omitted)
{space 23} {c |}
{space 16}country {c |}
{space 15}Austria  {c |}{col 25}{res}{space 2} .1355708{col 37}{space 2} .1795588{col 48}{space 1}    0.76{col 57}{space 3}0.450{col 65}{space 4} -.216377{col 78}{space 3} .4875185
{txt}{space 16}Brazil  {c |}{col 25}{res}{space 2} .2708292{col 37}{space 2} .0341348{col 48}{space 1}    7.93{col 57}{space 3}0.000{col 65}{space 4} .2039225{col 78}{space 3} .3377358
{txt}{space 16}France  {c |}{col 25}{res}{space 2}        0{col 37}{txt}  (omitted)
{space 15}Germany  {c |}{col 25}{res}{space 2} .2272503{col 37}{space 2}  .191136{col 48}{space 1}    1.19{col 57}{space 3}0.234{col 65}{space 4}-.1473895{col 78}{space 3} .6018901
{txt}{space 17}Italy  {c |}{col 25}{res}{space 2} .3687607{col 37}{space 2} .1784484{col 48}{space 1}    2.07{col 57}{space 3}0.039{col 65}{space 4} .0189895{col 78}{space 3} .7185319
{txt}{space 11}New_Zealand  {c |}{col 25}{res}{space 2}        0{col 37}{txt}  (omitted)
{space 16}Poland  {c |}{col 25}{res}{space 2}  .101857{col 37}{space 2} .0353207{col 48}{space 1}    2.88{col 57}{space 3}0.004{col 65}{space 4}  .032626{col 78}{space 3}  .171088
{txt}{space 17}Spain  {c |}{col 25}{res}{space 2}        0{col 37}{txt}  (omitted)
{space 16}Sweden  {c |}{col 25}{res}{space 2} .0452325{col 37}{space 2} .0367689{col 48}{space 1}    1.23{col 57}{space 3}0.219{col 65}{space 4}-.0268371{col 78}{space 3} .1173022
{txt}{space 20}UK  {c |}{col 25}{res}{space 2}        0{col 37}{txt}  (omitted)
{space 20}US  {c |}{col 25}{res}{space 2}        0{col 37}{txt}  (omitted)
{space 23} {c |}
{space 18}_cons {c |}{col 25}{res}{space 2} .2820728{col 37}{space 2} .0745849{col 48}{space 1}    3.78{col 57}{space 3}0.000{col 65}{space 4} .1358811{col 78}{space 3} .4282644
{txt}{hline 24}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{help ivreg2##idtest:Underidentification test} (Kleibergen-Paap rk LM statistic):{res}{col 71}  23.467
{txt}{col 52}Chi-sq({res}15{txt}) P-val =  {res}{col 73}0.0747
{txt}{hline 78}
{help ivreg2##widtest:Weak identification test} (Cragg-Donald Wald F statistic):{res}{col 71}   1.536
{txt}                         (Kleibergen-Paap rk Wald F statistic):{res}{col 71}   1.567
{txt}Stock-Yogo weak ID test critical values:{res}{txt}{col 42} 5% maximal IV relative bias{res}{col 73} 21.23
{txt}{col 42}10% maximal IV relative bias{res}{col 73} 11.51
{txt}{col 42}20% maximal IV relative bias{res}{col 73}  6.42
{txt}{col 42}30% maximal IV relative bias{res}{col 73}  4.63
{txt}{col 42}10% maximal IV size{res}{col 73} 50.39
{txt}{col 42}15% maximal IV size{res}{col 73} 26.80
{txt}{col 42}20% maximal IV size{res}{col 73} 18.72
{txt}{col 42}25% maximal IV size{res}{col 73} 14.60
{txt}Source: Stock-Yogo (2005).  Reproduced by permission.
NB: Critical values are for Cragg-Donald F statistic and i.i.d. errors.
{hline 78}
{help ivreg2##overidtests:Hansen J statistic} (overidentification test of all instruments):{res}{col 71}  15.824
{txt}{col 52}Chi-sq({res}14{txt}) P-val =  {res}{col 73}0.3243
{txt}{hline 78}
Instrumented:{col 23}satis_head
Included instruments:{col 23}thirties fourties fifties sixties seventies
{col 23}income2quartile income3quartile income4quartile
{col 23}incomenoanswer female highschool college noreligion
{col 23}christiannotcatholic catholic fulltimeworker
{col 23}parttimeworker unemployed selfemployed outofLF goodhealth
{col 23}white black latino asian whitecollar bluecollar
{col 23}serviceworker mv_incomenoanswer mv_highschool
{col 23}mv_noreligion mv_parttimeworker mv_selfemployed
{col 23}mv_goodhealth mv_white mv_whitecollar 2.country 3.country
{col 23}5.country 6.country 8.country 10.country
Excluded instruments:{col 23}TH1_TE1 TH1_TE2 TH1_TE3 TH1_TE4 TH2_TE1 TH2_TE2 TH2_TE3
{col 23}TH2_TE4 TH3_TE1 TH3_TE2 TH3_TE3 TH3_TE4 TH4_TE1 TH4_TE2
{col 23}TH4_TE3
Dropped collinear:{col 23}mv_thirties mv_fourties mv_fifties mv_sixties
{col 23}mv_seventies mv_income2quartile mv_income3quartile
{col 23}mv_income4quartile mv_female mv_college
{col 23}mv_christiannotcatholic mv_catholic mv_fulltimeworker
{col 23}mv_unemployed mv_outofLF mv_black mv_latino mv_asian
{col 23}mv_bluecollar mv_serviceworker 4.country 7.country
{col 23}9.country 11.country 12.country
{hline 78}
({res}est1{txt} stored)

{com}. 
. estadd scalar fstat = e(widstat)

{txt}added scalar:
              e(fstat) =  {res}1.5671569
{txt}
{com}. estadd local CFE "X", replace

{txt}added macro:
                e(CFE) : "{res:X}"

{com}. estadd local Controls "X", replace

{txt}added macro:
           e(Controls) : "{res:X}"

{com}. estadd local IV "16 IVs", replace

{txt}added macro:
                 e(IV) : "{res:16 IVs}"

{com}. get_mean

{txt}added scalar:
                 e(av) =  {res}.316
{txt}
{com}. 
. /* 4. Col 2: Strong leader with 1 instrument */ 
. * to get the Cragg-Donald statistic: 
. eststo: ivreg2 strong_leader ///
>         (satis_head = treatc) $controls $mv_controls  i.country, small robust 
{txt}Warning - collinearities detected
Vars dropped:{col 21}mv_thirties mv_fourties mv_fifties mv_sixties mv_seventies
{col 21}mv_income2quartile mv_income3quartile mv_income4quartile
{col 21}mv_female mv_college mv_christiannotcatholic mv_catholic
{col 21}mv_fulltimeworker mv_unemployed mv_outofLF mv_black
{col 21}mv_latino mv_asian mv_bluecollar mv_serviceworker 4.country
{col 21}7.country 9.country 11.country 12.country
{res}
{txt}IV (2SLS) estimation
{hline 20}

Estimates efficient for homoskedasticity only
Statistics robust to heteroskedasticity

{col 55}Number of obs = {res}   22535
{txt}{col 55}F( 43, 22491) = {res}   40.63
{txt}{col 55}Prob > F      = {res}  0.0000
{txt}Total (centered) SS     = {res} 4867.836077{txt}{col 55}Centered R2   = {res}  0.0605
{txt}Total (uncentered) SS   = {res}        7113{txt}{col 55}Uncentered R2 = {res}  0.3570
{txt}Residual SS             = {res} 4573.417974{txt}{col 55}Root MSE      = {res}   .4509

{txt}{hline 24}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 25}{c |}{col 37}    Robust
{col 1}          strong_leader{col 25}{c |} Coefficient{col 37}  std. err.{col 49}      t{col 57}   P>|t|{col 65}     [95% con{col 78}f. interval]
{hline 24}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 13}satis_head {c |}{col 25}{res}{space 2}-.0516437{col 37}{space 2} .4795475{col 48}{space 1}   -0.11{col 57}{space 3}0.914{col 65}{space 4}  -.99159{col 78}{space 3} .8883027
{txt}{space 15}thirties {c |}{col 25}{res}{space 2} .0357753{col 37}{space 2} .0129006{col 48}{space 1}    2.77{col 57}{space 3}0.006{col 65}{space 4} .0104893{col 78}{space 3} .0610613
{txt}{space 15}fourties {c |}{col 25}{res}{space 2}-.0079589{col 37}{space 2} .0187679{col 48}{space 1}   -0.42{col 57}{space 3}0.672{col 65}{space 4}-.0447453{col 78}{space 3} .0288276
{txt}{space 16}fifties {c |}{col 25}{res}{space 2}-.0479835{col 37}{space 2} .0196696{col 48}{space 1}   -2.44{col 57}{space 3}0.015{col 65}{space 4}-.0865373{col 78}{space 3}-.0094297
{txt}{space 16}sixties {c |}{col 25}{res}{space 2}-.0716678{col 37}{space 2} .0204268{col 48}{space 1}   -3.51{col 57}{space 3}0.000{col 65}{space 4}-.1117058{col 78}{space 3}-.0316298
{txt}{space 14}seventies {c |}{col 25}{res}{space 2}-.0759245{col 37}{space 2} .0144826{col 48}{space 1}   -5.24{col 57}{space 3}0.000{col 65}{space 4}-.1043114{col 78}{space 3}-.0475376
{txt}{space 8}income2quartile {c |}{col 25}{res}{space 2}-.0151673{col 37}{space 2} .0121628{col 48}{space 1}   -1.25{col 57}{space 3}0.212{col 65}{space 4}-.0390072{col 78}{space 3} .0086726
{txt}{space 8}income3quartile {c |}{col 25}{res}{space 2}-.0132255{col 37}{space 2} .0142349{col 48}{space 1}   -0.93{col 57}{space 3}0.353{col 65}{space 4} -.041127{col 78}{space 3} .0146759
{txt}{space 8}income4quartile {c |}{col 25}{res}{space 2}-.0374705{col 37}{space 2} .0218431{col 48}{space 1}   -1.72{col 57}{space 3}0.086{col 65}{space 4}-.0802846{col 78}{space 3} .0053436
{txt}{space 9}incomenoanswer {c |}{col 25}{res}{space 2}-.0055544{col 37}{space 2} .0146989{col 48}{space 1}   -0.38{col 57}{space 3}0.706{col 65}{space 4}-.0343653{col 78}{space 3} .0232565
{txt}{space 17}female {c |}{col 25}{res}{space 2}-.0209521{col 37}{space 2} .0079722{col 48}{space 1}   -2.63{col 57}{space 3}0.009{col 65}{space 4}-.0365781{col 78}{space 3}-.0053261
{txt}{space 13}highschool {c |}{col 25}{res}{space 2}-.1008705{col 37}{space 2} .0118098{col 48}{space 1}   -8.54{col 57}{space 3}0.000{col 65}{space 4}-.1240185{col 78}{space 3}-.0777226
{txt}{space 16}college {c |}{col 25}{res}{space 2}-.1640593{col 37}{space 2} .0102753{col 48}{space 1}  -15.97{col 57}{space 3}0.000{col 65}{space 4}-.1841995{col 78}{space 3}-.1439191
{txt}{space 13}noreligion {c |}{col 25}{res}{space 2}-.1419263{col 37}{space 2} .0202419{col 48}{space 1}   -7.01{col 57}{space 3}0.000{col 65}{space 4}-.1816018{col 78}{space 3}-.1022508
{txt}{space 3}christiannotcatholic {c |}{col 25}{res}{space 2}-.0694095{col 37}{space 2} .0343234{col 48}{space 1}   -2.02{col 57}{space 3}0.043{col 65}{space 4}-.1366857{col 78}{space 3}-.0021333
{txt}{space 15}catholic {c |}{col 25}{res}{space 2}-.0308272{col 37}{space 2} .0174993{col 48}{space 1}   -1.76{col 57}{space 3}0.078{col 65}{space 4}-.0651271{col 78}{space 3} .0034727
{txt}{space 9}fulltimeworker {c |}{col 25}{res}{space 2}-.0647152{col 37}{space 2} .1508798{col 48}{space 1}   -0.43{col 57}{space 3}0.668{col 65}{space 4}-.3604501{col 78}{space 3} .2310198
{txt}{space 9}parttimeworker {c |}{col 25}{res}{space 2}-.1140204{col 37}{space 2} .1518095{col 48}{space 1}   -0.75{col 57}{space 3}0.453{col 65}{space 4}-.4115776{col 78}{space 3} .1835369
{txt}{space 13}unemployed {c |}{col 25}{res}{space 2}-.0990461{col 37}{space 2} .1679585{col 48}{space 1}   -0.59{col 57}{space 3}0.555{col 65}{space 4}-.4282563{col 78}{space 3} .2301642
{txt}{space 11}selfemployed {c |}{col 25}{res}{space 2} -.035904{col 37}{space 2} .1855851{col 48}{space 1}   -0.19{col 57}{space 3}0.847{col 65}{space 4}-.3996638{col 78}{space 3} .3278558
{txt}{space 16}outofLF {c |}{col 25}{res}{space 2}-.0518076{col 37}{space 2} .1568185{col 48}{space 1}   -0.33{col 57}{space 3}0.741{col 65}{space 4}-.3591827{col 78}{space 3} .2555675
{txt}{space 13}goodhealth {c |}{col 25}{res}{space 2} .0064713{col 37}{space 2} .0248919{col 48}{space 1}    0.26{col 57}{space 3}0.795{col 65}{space 4}-.0423186{col 78}{space 3} .0552612
{txt}{space 18}white {c |}{col 25}{res}{space 2} .0511227{col 37}{space 2}   .08873{col 48}{space 1}    0.58{col 57}{space 3}0.565{col 65}{space 4}-.1227942{col 78}{space 3} .2250397
{txt}{space 18}black {c |}{col 25}{res}{space 2} .1433034{col 37}{space 2} .0708454{col 48}{space 1}    2.02{col 57}{space 3}0.043{col 65}{space 4} .0044415{col 78}{space 3} .2821654
{txt}{space 17}latino {c |}{col 25}{res}{space 2} .1785114{col 37}{space 2} .0790821{col 48}{space 1}    2.26{col 57}{space 3}0.024{col 65}{space 4}  .023505{col 78}{space 3} .3335179
{txt}{space 18}asian {c |}{col 25}{res}{space 2} .1462388{col 37}{space 2} .0845425{col 48}{space 1}    1.73{col 57}{space 3}0.084{col 65}{space 4}-.0194703{col 78}{space 3} .3119479
{txt}{space 12}whitecollar {c |}{col 25}{res}{space 2}  .003349{col 37}{space 2} .0167035{col 48}{space 1}    0.20{col 57}{space 3}0.841{col 65}{space 4}-.0293911{col 78}{space 3}  .036089
{txt}{space 13}bluecollar {c |}{col 25}{res}{space 2} .0405661{col 37}{space 2} .0195791{col 48}{space 1}    2.07{col 57}{space 3}0.038{col 65}{space 4} .0021896{col 78}{space 3} .0789425
{txt}{space 10}serviceworker {c |}{col 25}{res}{space 2}  .015369{col 37}{space 2} .0131808{col 48}{space 1}    1.17{col 57}{space 3}0.244{col 65}{space 4}-.0104663{col 78}{space 3} .0412043
{txt}{space 12}mv_thirties {c |}{col 25}{res}{space 2}        0{col 37}{txt}  (omitted)
{space 12}mv_fourties {c |}{col 25}{res}{space 2}        0{col 37}{txt}  (omitted)
{space 13}mv_fifties {c |}{col 25}{res}{space 2}        0{col 37}{txt}  (omitted)
{space 13}mv_sixties {c |}{col 25}{res}{space 2}        0{col 37}{txt}  (omitted)
{space 11}mv_seventies {c |}{col 25}{res}{space 2}        0{col 37}{txt}  (omitted)
{space 5}mv_income2quartile {c |}{col 25}{res}{space 2}        0{col 37}{txt}  (omitted)
{space 5}mv_income3quartile {c |}{col 25}{res}{space 2}        0{col 37}{txt}  (omitted)
{space 5}mv_income4quartile {c |}{col 25}{res}{space 2}        0{col 37}{txt}  (omitted)
{space 6}mv_incomenoanswer {c |}{col 25}{res}{space 2} .0912752{col 37}{space 2} .0419654{col 48}{space 1}    2.18{col 57}{space 3}0.030{col 65}{space 4}   .00902{col 78}{space 3} .1735303
{txt}{space 14}mv_female {c |}{col 25}{res}{space 2}        0{col 37}{txt}  (omitted)
{space 10}mv_highschool {c |}{col 25}{res}{space 2}-.0621757{col 37}{space 2} .0271647{col 48}{space 1}   -2.29{col 57}{space 3}0.022{col 65}{space 4}-.1154204{col 78}{space 3}-.0089311
{txt}{space 13}mv_college {c |}{col 25}{res}{space 2}        0{col 37}{txt}  (omitted)
{space 10}mv_noreligion {c |}{col 25}{res}{space 2}-.0292526{col 37}{space 2} .0463462{col 48}{space 1}   -0.63{col 57}{space 3}0.528{col 65}{space 4}-.1200944{col 78}{space 3} .0615892
{txt}mv_christiannotcatholic {c |}{col 25}{res}{space 2}        0{col 37}{txt}  (omitted)
{space 12}mv_catholic {c |}{col 25}{res}{space 2}        0{col 37}{txt}  (omitted)
{space 6}mv_fulltimeworker {c |}{col 25}{res}{space 2}        0{col 37}{txt}  (omitted)
{space 6}mv_parttimeworker {c |}{col 25}{res}{space 2}-.0604384{col 37}{space 2} .1419235{col 48}{space 1}   -0.43{col 57}{space 3}0.670{col 65}{space 4}-.3386184{col 78}{space 3} .2177416
{txt}{space 10}mv_unemployed {c |}{col 25}{res}{space 2}        0{col 37}{txt}  (omitted)
{space 8}mv_selfemployed {c |}{col 25}{res}{space 2} .0904599{col 37}{space 2} .1458341{col 48}{space 1}    0.62{col 57}{space 3}0.535{col 65}{space 4}-.1953851{col 78}{space 3} .3763049
{txt}{space 13}mv_outofLF {c |}{col 25}{res}{space 2}        0{col 37}{txt}  (omitted)
{space 10}mv_goodhealth {c |}{col 25}{res}{space 2} .2064834{col 37}{space 2} .1683784{col 48}{space 1}    1.23{col 57}{space 3}0.220{col 65}{space 4}  -.12355{col 78}{space 3} .5365168
{txt}{space 15}mv_white {c |}{col 25}{res}{space 2} .1498555{col 37}{space 2} .1928091{col 48}{space 1}    0.78{col 57}{space 3}0.437{col 65}{space 4}-.2280638{col 78}{space 3} .5277747
{txt}{space 15}mv_black {c |}{col 25}{res}{space 2}        0{col 37}{txt}  (omitted)
{space 14}mv_latino {c |}{col 25}{res}{space 2}        0{col 37}{txt}  (omitted)
{space 15}mv_asian {c |}{col 25}{res}{space 2}        0{col 37}{txt}  (omitted)
{space 9}mv_whitecollar {c |}{col 25}{res}{space 2} .1743704{col 37}{space 2} .1494928{col 48}{space 1}    1.17{col 57}{space 3}0.243{col 65}{space 4}-.1186459{col 78}{space 3} .4673868
{txt}{space 10}mv_bluecollar {c |}{col 25}{res}{space 2}        0{col 37}{txt}  (omitted)
{space 7}mv_serviceworker {c |}{col 25}{res}{space 2}        0{col 37}{txt}  (omitted)
{space 23} {c |}
{space 16}country {c |}
{space 15}Austria  {c |}{col 25}{res}{space 2} .0774431{col 37}{space 2} .2561465{col 48}{space 1}    0.30{col 57}{space 3}0.762{col 65}{space 4}-.4246219{col 78}{space 3} .5795081
{txt}{space 16}Brazil  {c |}{col 25}{res}{space 2} .2726554{col 37}{space 2} .0341491{col 48}{space 1}    7.98{col 57}{space 3}0.000{col 65}{space 4} .2057208{col 78}{space 3}   .33959
{txt}{space 16}France  {c |}{col 25}{res}{space 2}        0{col 37}{txt}  (omitted)
{space 15}Germany  {c |}{col 25}{res}{space 2} .1647125{col 37}{space 2} .2743439{col 48}{space 1}    0.60{col 57}{space 3}0.548{col 65}{space 4}-.3730207{col 78}{space 3} .7024457
{txt}{space 17}Italy  {c |}{col 25}{res}{space 2} .3134161{col 37}{space 2} .2492773{col 48}{space 1}    1.26{col 57}{space 3}0.209{col 65}{space 4}-.1751847{col 78}{space 3} .8020169
{txt}{space 11}New_Zealand  {c |}{col 25}{res}{space 2}        0{col 37}{txt}  (omitted)
{space 16}Poland  {c |}{col 25}{res}{space 2} .1058037{col 37}{space 2} .0370528{col 48}{space 1}    2.86{col 57}{space 3}0.004{col 65}{space 4} .0331776{col 78}{space 3} .1784297
{txt}{space 17}Spain  {c |}{col 25}{res}{space 2}        0{col 37}{txt}  (omitted)
{space 16}Sweden  {c |}{col 25}{res}{space 2} .0386584{col 37}{space 2} .0421116{col 48}{space 1}    0.92{col 57}{space 3}0.359{col 65}{space 4}-.0438834{col 78}{space 3} .1212001
{txt}{space 20}UK  {c |}{col 25}{res}{space 2}        0{col 37}{txt}  (omitted)
{space 20}US  {c |}{col 25}{res}{space 2}        0{col 37}{txt}  (omitted)
{space 23} {c |}
{space 18}_cons {c |}{col 25}{res}{space 2} .2829228{col 37}{space 2} .0734565{col 48}{space 1}    3.85{col 57}{space 3}0.000{col 65}{space 4} .1389428{col 78}{space 3} .4269027
{txt}{hline 24}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{help ivreg2##idtest:Underidentification test} (Kleibergen-Paap rk LM statistic):{res}{col 71}  10.118
{txt}{col 52}Chi-sq({res}1{txt}) P-val =  {res}{col 73}0.0015
{txt}{hline 78}
{help ivreg2##widtest:Weak identification test} (Cragg-Donald Wald F statistic):{res}{col 71}  10.084
{txt}                         (Kleibergen-Paap rk Wald F statistic):{res}{col 71}  10.114
{txt}Stock-Yogo weak ID test critical values:{res}{txt}{col 42}10% maximal IV size{res}{col 73} 16.38
{txt}{col 42}15% maximal IV size{res}{col 73}  8.96
{txt}{col 42}20% maximal IV size{res}{col 73}  6.66
{txt}{col 42}25% maximal IV size{res}{col 73}  5.53
{txt}Source: Stock-Yogo (2005).  Reproduced by permission.
NB: Critical values are for Cragg-Donald F statistic and i.i.d. errors.
{hline 78}
{help ivreg2##overidtests:Hansen J statistic} (overidentification test of all instruments):{res}{col 71}   0.000
{txt}{col 50}(equation exactly identified)
{hline 78}
Instrumented:{col 23}satis_head
Included instruments:{col 23}thirties fourties fifties sixties seventies
{col 23}income2quartile income3quartile income4quartile
{col 23}incomenoanswer female highschool college noreligion
{col 23}christiannotcatholic catholic fulltimeworker
{col 23}parttimeworker unemployed selfemployed outofLF goodhealth
{col 23}white black latino asian whitecollar bluecollar
{col 23}serviceworker mv_incomenoanswer mv_highschool
{col 23}mv_noreligion mv_parttimeworker mv_selfemployed
{col 23}mv_goodhealth mv_white mv_whitecollar 2.country 3.country
{col 23}5.country 6.country 8.country 10.country
Excluded instruments:{col 23}treatc
Dropped collinear:{col 23}mv_thirties mv_fourties mv_fifties mv_sixties
{col 23}mv_seventies mv_income2quartile mv_income3quartile
{col 23}mv_income4quartile mv_female mv_college
{col 23}mv_christiannotcatholic mv_catholic mv_fulltimeworker
{col 23}mv_unemployed mv_outofLF mv_black mv_latino mv_asian
{col 23}mv_bluecollar mv_serviceworker 4.country 7.country
{col 23}9.country 11.country 12.country
{hline 78}
({res}est2{txt} stored)

{com}. 
. estadd scalar fstat = e(widstat)

{txt}added scalar:
              e(fstat) =  {res}10.113691
{txt}
{com}. estadd local CFE "X", replace

{txt}added macro:
                e(CFE) : "{res:X}"

{com}. estadd local Controls "X", replace

{txt}added macro:
           e(Controls) : "{res:X}"

{com}. estadd local IV "SumIV", replace

{txt}added macro:
                 e(IV) : "{res:SumIV}"

{com}. get_mean

{txt}added scalar:
                 e(av) =  {res}.316
{txt}
{com}. 
. /* 4. Col 3: Experts with 16 instruments */     
. * to get the Cragg-Donald statistic: 
. 
. eststo: ivreg2 experts ///
>         (satis_head = TH1_TE1 TH1_TE2 TH1_TE3 TH1_TE4 TH2_TE1 TH2_TE2 TH2_TE3 TH2_TE4 TH3_TE1 TH3_TE2 TH3_TE3 TH3_TE4 TH4_TE1 TH4_TE2 TH4_TE3) $controls $mv_controls  i.country, small robust 
{txt}Warning - collinearities detected
Vars dropped:{col 21}mv_thirties mv_fourties mv_fifties mv_sixties mv_seventies
{col 21}mv_income2quartile mv_income3quartile mv_income4quartile
{col 21}mv_female mv_college mv_christiannotcatholic mv_catholic
{col 21}mv_fulltimeworker mv_unemployed mv_outofLF mv_black
{col 21}mv_latino mv_asian mv_bluecollar mv_serviceworker 4.country
{col 21}7.country 9.country 11.country 12.country
{res}
{txt}IV (2SLS) estimation
{hline 20}

Estimates efficient for homoskedasticity only
Statistics robust to heteroskedasticity

{col 55}Number of obs = {res}   22537
{txt}{col 55}F( 43, 22493) = {res}   31.15
{txt}{col 55}Prob > F      = {res}  0.0000
{txt}Total (centered) SS     = {res} 5450.407419{txt}{col 55}Centered R2   = {res}  0.0466
{txt}Total (uncentered) SS   = {res}       13304{txt}{col 55}Uncentered R2 = {res}  0.6094
{txt}Residual SS             = {res} 5196.318152{txt}{col 55}Root MSE      = {res}   .4806

{txt}{hline 24}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 25}{c |}{col 37}    Robust
{col 1}                experts{col 25}{c |} Coefficient{col 37}  std. err.{col 49}      t{col 57}   P>|t|{col 65}     [95% con{col 78}f. interval]
{hline 24}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 13}satis_head {c |}{col 25}{res}{space 2} .0456635{col 37}{space 2} .3411078{col 48}{space 1}    0.13{col 57}{space 3}0.894{col 65}{space 4}-.6229316{col 78}{space 3} .7142585
{txt}{space 15}thirties {c |}{col 25}{res}{space 2}-.0094376{col 37}{space 2} .0119951{col 48}{space 1}   -0.79{col 57}{space 3}0.431{col 65}{space 4}-.0329488{col 78}{space 3} .0140736
{txt}{space 15}fourties {c |}{col 25}{res}{space 2}-.0761827{col 37}{space 2} .0155016{col 48}{space 1}   -4.91{col 57}{space 3}0.000{col 65}{space 4}-.1065669{col 78}{space 3}-.0457985
{txt}{space 16}fifties {c |}{col 25}{res}{space 2} -.111644{col 37}{space 2}  .016346{col 48}{space 1}   -6.83{col 57}{space 3}0.000{col 65}{space 4}-.1436832{col 78}{space 3}-.0796048
{txt}{space 16}sixties {c |}{col 25}{res}{space 2}-.1731099{col 37}{space 2} .0168219{col 48}{space 1}  -10.29{col 57}{space 3}0.000{col 65}{space 4}-.2060819{col 78}{space 3}-.1401379
{txt}{space 14}seventies {c |}{col 25}{res}{space 2}-.1909058{col 37}{space 2} .0147151{col 48}{space 1}  -12.97{col 57}{space 3}0.000{col 65}{space 4}-.2197484{col 78}{space 3}-.1620632
{txt}{space 8}income2quartile {c |}{col 25}{res}{space 2} .0034822{col 37}{space 2} .0111297{col 48}{space 1}    0.31{col 57}{space 3}0.754{col 65}{space 4}-.0183328{col 78}{space 3} .0252972
{txt}{space 8}income3quartile {c |}{col 25}{res}{space 2}   .02275{col 37}{space 2} .0124516{col 48}{space 1}    1.83{col 57}{space 3}0.068{col 65}{space 4}-.0016559{col 78}{space 3}  .047156
{txt}{space 8}income4quartile {c |}{col 25}{res}{space 2}-.0023691{col 37}{space 2} .0171739{col 48}{space 1}   -0.14{col 57}{space 3}0.890{col 65}{space 4}-.0360311{col 78}{space 3}  .031293
{txt}{space 9}incomenoanswer {c |}{col 25}{res}{space 2}-.0095551{col 37}{space 2} .0154725{col 48}{space 1}   -0.62{col 57}{space 3}0.537{col 65}{space 4}-.0398822{col 78}{space 3} .0207721
{txt}{space 17}female {c |}{col 25}{res}{space 2} .0361502{col 37}{space 2} .0074681{col 48}{space 1}    4.84{col 57}{space 3}0.000{col 65}{space 4} .0215122{col 78}{space 3} .0507882
{txt}{space 13}highschool {c |}{col 25}{res}{space 2}-.0470622{col 37}{space 2} .0115957{col 48}{space 1}   -4.06{col 57}{space 3}0.000{col 65}{space 4}-.0697906{col 78}{space 3}-.0243339
{txt}{space 16}college {c |}{col 25}{res}{space 2}-.0400389{col 37}{space 2} .0105351{col 48}{space 1}   -3.80{col 57}{space 3}0.000{col 65}{space 4}-.0606884{col 78}{space 3}-.0193894
{txt}{space 13}noreligion {c |}{col 25}{res}{space 2}-.0285121{col 37}{space 2} .0171879{col 48}{space 1}   -1.66{col 57}{space 3}0.097{col 65}{space 4}-.0622015{col 78}{space 3} .0051773
{txt}{space 3}christiannotcatholic {c |}{col 25}{res}{space 2}-.0664693{col 37}{space 2} .0266917{col 48}{space 1}   -2.49{col 57}{space 3}0.013{col 65}{space 4}-.1187868{col 78}{space 3}-.0141518
{txt}{space 15}catholic {c |}{col 25}{res}{space 2}-.0241967{col 37}{space 2} .0157111{col 48}{space 1}   -1.54{col 57}{space 3}0.124{col 65}{space 4}-.0549917{col 78}{space 3} .0065982
{txt}{space 9}fulltimeworker {c |}{col 25}{res}{space 2}-.0246673{col 37}{space 2} .1082013{col 48}{space 1}   -0.23{col 57}{space 3}0.820{col 65}{space 4}-.2367494{col 78}{space 3} .1874148
{txt}{space 9}parttimeworker {c |}{col 25}{res}{space 2}-.0506536{col 37}{space 2} .1107081{col 48}{space 1}   -0.46{col 57}{space 3}0.647{col 65}{space 4} -.267649{col 78}{space 3} .1663419
{txt}{space 13}unemployed {c |}{col 25}{res}{space 2} -.060224{col 37}{space 2} .1211769{col 48}{space 1}   -0.50{col 57}{space 3}0.619{col 65}{space 4}-.2977392{col 78}{space 3} .1772912
{txt}{space 11}selfemployed {c |}{col 25}{res}{space 2}-.0143552{col 37}{space 2} .1350469{col 48}{space 1}   -0.11{col 57}{space 3}0.915{col 65}{space 4}-.2790564{col 78}{space 3} .2503461
{txt}{space 16}outofLF {c |}{col 25}{res}{space 2}-.0381367{col 37}{space 2} .1124823{col 48}{space 1}   -0.34{col 57}{space 3}0.735{col 65}{space 4}-.2586098{col 78}{space 3} .1823364
{txt}{space 13}goodhealth {c |}{col 25}{res}{space 2}-.0102943{col 37}{space 2}  .018505{col 48}{space 1}   -0.56{col 57}{space 3}0.578{col 65}{space 4}-.0465654{col 78}{space 3} .0259768
{txt}{space 18}white {c |}{col 25}{res}{space 2} .0788894{col 37}{space 2} .0802715{col 48}{space 1}    0.98{col 57}{space 3}0.326{col 65}{space 4}-.0784484{col 78}{space 3} .2362273
{txt}{space 18}black {c |}{col 25}{res}{space 2} .1228081{col 37}{space 2} .0729307{col 48}{space 1}    1.68{col 57}{space 3}0.092{col 65}{space 4}-.0201412{col 78}{space 3} .2657575
{txt}{space 17}latino {c |}{col 25}{res}{space 2} .1622047{col 37}{space 2} .0784315{col 48}{space 1}    2.07{col 57}{space 3}0.039{col 65}{space 4} .0084735{col 78}{space 3} .3159358
{txt}{space 18}asian {c |}{col 25}{res}{space 2} .2207254{col 37}{space 2} .0807153{col 48}{space 1}    2.73{col 57}{space 3}0.006{col 65}{space 4} .0625178{col 78}{space 3}  .378933
{txt}{space 12}whitecollar {c |}{col 25}{res}{space 2} .0287595{col 37}{space 2} .0173957{col 48}{space 1}    1.65{col 57}{space 3}0.098{col 65}{space 4}-.0053372{col 78}{space 3} .0628562
{txt}{space 13}bluecollar {c |}{col 25}{res}{space 2} .0083387{col 37}{space 2} .0189497{col 48}{space 1}    0.44{col 57}{space 3}0.660{col 65}{space 4}-.0288041{col 78}{space 3} .0454815
{txt}{space 10}serviceworker {c |}{col 25}{res}{space 2} .0007801{col 37}{space 2}  .014265{col 48}{space 1}    0.05{col 57}{space 3}0.956{col 65}{space 4}-.0271803{col 78}{space 3} .0287405
{txt}{space 12}mv_thirties {c |}{col 25}{res}{space 2}        0{col 37}{txt}  (omitted)
{space 12}mv_fourties {c |}{col 25}{res}{space 2}        0{col 37}{txt}  (omitted)
{space 13}mv_fifties {c |}{col 25}{res}{space 2}        0{col 37}{txt}  (omitted)
{space 13}mv_sixties {c |}{col 25}{res}{space 2}        0{col 37}{txt}  (omitted)
{space 11}mv_seventies {c |}{col 25}{res}{space 2}        0{col 37}{txt}  (omitted)
{space 5}mv_income2quartile {c |}{col 25}{res}{space 2}        0{col 37}{txt}  (omitted)
{space 5}mv_income3quartile {c |}{col 25}{res}{space 2}        0{col 37}{txt}  (omitted)
{space 5}mv_income4quartile {c |}{col 25}{res}{space 2}        0{col 37}{txt}  (omitted)
{space 6}mv_incomenoanswer {c |}{col 25}{res}{space 2} .0753297{col 37}{space 2} .0333865{col 48}{space 1}    2.26{col 57}{space 3}0.024{col 65}{space 4} .0098898{col 78}{space 3} .1407697
{txt}{space 14}mv_female {c |}{col 25}{res}{space 2}        0{col 37}{txt}  (omitted)
{space 10}mv_highschool {c |}{col 25}{res}{space 2}-.0450139{col 37}{space 2} .0275413{col 48}{space 1}   -1.63{col 57}{space 3}0.102{col 65}{space 4}-.0989967{col 78}{space 3} .0089688
{txt}{space 13}mv_college {c |}{col 25}{res}{space 2}        0{col 37}{txt}  (omitted)
{space 10}mv_noreligion {c |}{col 25}{res}{space 2} .0274345{col 37}{space 2} .0382037{col 48}{space 1}    0.72{col 57}{space 3}0.473{col 65}{space 4}-.0474474{col 78}{space 3} .1023163
{txt}mv_christiannotcatholic {c |}{col 25}{res}{space 2}        0{col 37}{txt}  (omitted)
{space 12}mv_catholic {c |}{col 25}{res}{space 2}        0{col 37}{txt}  (omitted)
{space 6}mv_fulltimeworker {c |}{col 25}{res}{space 2}        0{col 37}{txt}  (omitted)
{space 6}mv_parttimeworker {c |}{col 25}{res}{space 2}-.0436382{col 37}{space 2}  .103359{col 48}{space 1}   -0.42{col 57}{space 3}0.673{col 65}{space 4}-.2462291{col 78}{space 3} .1589527
{txt}{space 10}mv_unemployed {c |}{col 25}{res}{space 2}        0{col 37}{txt}  (omitted)
{space 8}mv_selfemployed {c |}{col 25}{res}{space 2}-.1978746{col 37}{space 2} .1061374{col 48}{space 1}   -1.86{col 57}{space 3}0.062{col 65}{space 4}-.4059112{col 78}{space 3}  .010162
{txt}{space 13}mv_outofLF {c |}{col 25}{res}{space 2}        0{col 37}{txt}  (omitted)
{space 10}mv_goodhealth {c |}{col 25}{res}{space 2} .1796053{col 37}{space 2} .1335618{col 48}{space 1}    1.34{col 57}{space 3}0.179{col 65}{space 4}-.0821852{col 78}{space 3} .4413958
{txt}{space 15}mv_white {c |}{col 25}{res}{space 2} .1369439{col 37}{space 2} .1461322{col 48}{space 1}    0.94{col 57}{space 3}0.349{col 65}{space 4}-.1494853{col 78}{space 3} .4233731
{txt}{space 15}mv_black {c |}{col 25}{res}{space 2}        0{col 37}{txt}  (omitted)
{space 14}mv_latino {c |}{col 25}{res}{space 2}        0{col 37}{txt}  (omitted)
{space 15}mv_asian {c |}{col 25}{res}{space 2}        0{col 37}{txt}  (omitted)
{space 9}mv_whitecollar {c |}{col 25}{res}{space 2}-.0436677{col 37}{space 2} .1093938{col 48}{space 1}   -0.40{col 57}{space 3}0.690{col 65}{space 4}-.2580871{col 78}{space 3} .1707518
{txt}{space 10}mv_bluecollar {c |}{col 25}{res}{space 2}        0{col 37}{txt}  (omitted)
{space 7}mv_serviceworker {c |}{col 25}{res}{space 2}        0{col 37}{txt}  (omitted)
{space 23} {c |}
{space 16}country {c |}
{space 15}Austria  {c |}{col 25}{res}{space 2} .2101316{col 37}{space 2}  .190953{col 48}{space 1}    1.10{col 57}{space 3}0.271{col 65}{space 4}-.1641496{col 78}{space 3} .5844128
{txt}{space 16}Brazil  {c |}{col 25}{res}{space 2} .0156814{col 37}{space 2} .0343899{col 48}{space 1}    0.46{col 57}{space 3}0.648{col 65}{space 4}-.0517251{col 78}{space 3}  .083088
{txt}{space 16}France  {c |}{col 25}{res}{space 2}        0{col 37}{txt}  (omitted)
{space 15}Germany  {c |}{col 25}{res}{space 2}   .02615{col 37}{space 2} .2031203{col 48}{space 1}    0.13{col 57}{space 3}0.898{col 65}{space 4}-.3719799{col 78}{space 3}   .42428
{txt}{space 17}Italy  {c |}{col 25}{res}{space 2} .0183206{col 37}{space 2} .1865887{col 48}{space 1}    0.10{col 57}{space 3}0.922{col 65}{space 4}-.3474062{col 78}{space 3} .3840474
{txt}{space 11}New_Zealand  {c |}{col 25}{res}{space 2}        0{col 37}{txt}  (omitted)
{space 16}Poland  {c |}{col 25}{res}{space 2}-.0112436{col 37}{space 2} .0365311{col 48}{space 1}   -0.31{col 57}{space 3}0.758{col 65}{space 4}-.0828471{col 78}{space 3} .0603599
{txt}{space 17}Spain  {c |}{col 25}{res}{space 2}        0{col 37}{txt}  (omitted)
{space 16}Sweden  {c |}{col 25}{res}{space 2}-.1796588{col 37}{space 2} .0395116{col 48}{space 1}   -4.55{col 57}{space 3}0.000{col 65}{space 4}-.2571043{col 78}{space 3}-.1022134
{txt}{space 20}UK  {c |}{col 25}{res}{space 2}        0{col 37}{txt}  (omitted)
{space 20}US  {c |}{col 25}{res}{space 2}        0{col 37}{txt}  (omitted)
{space 23} {c |}
{space 18}_cons {c |}{col 25}{res}{space 2} .8496176{col 37}{space 2} .0765797{col 48}{space 1}   11.09{col 57}{space 3}0.000{col 65}{space 4}  .699516{col 78}{space 3} .9997191
{txt}{hline 24}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{help ivreg2##idtest:Underidentification test} (Kleibergen-Paap rk LM statistic):{res}{col 71}  23.461
{txt}{col 52}Chi-sq({res}15{txt}) P-val =  {res}{col 73}0.0748
{txt}{hline 78}
{help ivreg2##widtest:Weak identification test} (Cragg-Donald Wald F statistic):{res}{col 71}   1.536
{txt}                         (Kleibergen-Paap rk Wald F statistic):{res}{col 71}   1.567
{txt}Stock-Yogo weak ID test critical values:{res}{txt}{col 42} 5% maximal IV relative bias{res}{col 73} 21.23
{txt}{col 42}10% maximal IV relative bias{res}{col 73} 11.51
{txt}{col 42}20% maximal IV relative bias{res}{col 73}  6.42
{txt}{col 42}30% maximal IV relative bias{res}{col 73}  4.63
{txt}{col 42}10% maximal IV size{res}{col 73} 50.39
{txt}{col 42}15% maximal IV size{res}{col 73} 26.80
{txt}{col 42}20% maximal IV size{res}{col 73} 18.72
{txt}{col 42}25% maximal IV size{res}{col 73} 14.60
{txt}Source: Stock-Yogo (2005).  Reproduced by permission.
NB: Critical values are for Cragg-Donald F statistic and i.i.d. errors.
{hline 78}
{help ivreg2##overidtests:Hansen J statistic} (overidentification test of all instruments):{res}{col 71}  18.625
{txt}{col 52}Chi-sq({res}14{txt}) P-val =  {res}{col 73}0.1798
{txt}{hline 78}
Instrumented:{col 23}satis_head
Included instruments:{col 23}thirties fourties fifties sixties seventies
{col 23}income2quartile income3quartile income4quartile
{col 23}incomenoanswer female highschool college noreligion
{col 23}christiannotcatholic catholic fulltimeworker
{col 23}parttimeworker unemployed selfemployed outofLF goodhealth
{col 23}white black latino asian whitecollar bluecollar
{col 23}serviceworker mv_incomenoanswer mv_highschool
{col 23}mv_noreligion mv_parttimeworker mv_selfemployed
{col 23}mv_goodhealth mv_white mv_whitecollar 2.country 3.country
{col 23}5.country 6.country 8.country 10.country
Excluded instruments:{col 23}TH1_TE1 TH1_TE2 TH1_TE3 TH1_TE4 TH2_TE1 TH2_TE2 TH2_TE3
{col 23}TH2_TE4 TH3_TE1 TH3_TE2 TH3_TE3 TH3_TE4 TH4_TE1 TH4_TE2
{col 23}TH4_TE3
Dropped collinear:{col 23}mv_thirties mv_fourties mv_fifties mv_sixties
{col 23}mv_seventies mv_income2quartile mv_income3quartile
{col 23}mv_income4quartile mv_female mv_college
{col 23}mv_christiannotcatholic mv_catholic mv_fulltimeworker
{col 23}mv_unemployed mv_outofLF mv_black mv_latino mv_asian
{col 23}mv_bluecollar mv_serviceworker 4.country 7.country
{col 23}9.country 11.country 12.country
{hline 78}
({res}est3{txt} stored)

{com}. 
. estadd scalar fstat = e(widstat)

{txt}added scalar:
              e(fstat) =  {res}1.5666211
{txt}
{com}. estadd local CFE "X", replace

{txt}added macro:
                e(CFE) : "{res:X}"

{com}. estadd local IV "16 IVs", replace

{txt}added macro:
                 e(IV) : "{res:16 IVs}"

{com}. estadd local Controls "X", replace

{txt}added macro:
           e(Controls) : "{res:X}"

{com}. get_mean

{txt}added scalar:
                 e(av) =  {res}.59
{txt}
{com}. 
. /* 4. Col 4: Experts with 1 instrument */       
. * to get the Cragg-Donald statistic: 
. eststo: ivreg2 experts ///
>         (satis_head = treatc ) $controls $mv_controls  i.country, small robust 
{txt}Warning - collinearities detected
Vars dropped:{col 21}mv_thirties mv_fourties mv_fifties mv_sixties mv_seventies
{col 21}mv_income2quartile mv_income3quartile mv_income4quartile
{col 21}mv_female mv_college mv_christiannotcatholic mv_catholic
{col 21}mv_fulltimeworker mv_unemployed mv_outofLF mv_black
{col 21}mv_latino mv_asian mv_bluecollar mv_serviceworker 4.country
{col 21}7.country 9.country 11.country 12.country
{res}
{txt}IV (2SLS) estimation
{hline 20}

Estimates efficient for homoskedasticity only
Statistics robust to heteroskedasticity

{col 55}Number of obs = {res}   22537
{txt}{col 55}F( 43, 22493) = {res}   31.33
{txt}{col 55}Prob > F      = {res}  0.0000
{txt}Total (centered) SS     = {res} 5450.407419{txt}{col 55}Centered R2   = {res}  0.0507
{txt}Total (uncentered) SS   = {res}       13304{txt}{col 55}Uncentered R2 = {res}  0.6111
{txt}Residual SS             = {res}  5173.81049{txt}{col 55}Root MSE      = {res}   .4796

{txt}{hline 24}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 25}{c |}{col 37}    Robust
{col 1}                experts{col 25}{c |} Coefficient{col 37}  std. err.{col 49}      t{col 57}   P>|t|{col 65}     [95% con{col 78}f. interval]
{hline 24}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 13}satis_head {c |}{col 25}{res}{space 2} -.009052{col 37}{space 2} .5138418{col 48}{space 1}   -0.02{col 57}{space 3}0.986{col 65}{space 4}-1.016218{col 78}{space 3} .9981136
{txt}{space 15}thirties {c |}{col 25}{res}{space 2}-.0101661{col 37}{space 2} .0130115{col 48}{space 1}   -0.78{col 57}{space 3}0.435{col 65}{space 4}-.0356696{col 78}{space 3} .0153374
{txt}{space 15}fourties {c |}{col 25}{res}{space 2}-.0779206{col 37}{space 2} .0197178{col 48}{space 1}   -3.95{col 57}{space 3}0.000{col 65}{space 4}-.1165688{col 78}{space 3}-.0392723
{txt}{space 16}fifties {c |}{col 25}{res}{space 2}-.1134967{col 37}{space 2} .0208603{col 48}{space 1}   -5.44{col 57}{space 3}0.000{col 65}{space 4}-.1543842{col 78}{space 3}-.0726091
{txt}{space 16}sixties {c |}{col 25}{res}{space 2} -.175063{col 37}{space 2} .0216787{col 48}{space 1}   -8.08{col 57}{space 3}0.000{col 65}{space 4}-.2175547{col 78}{space 3}-.1325712
{txt}{space 14}seventies {c |}{col 25}{res}{space 2}-.1916411{col 37}{space 2} .0155533{col 48}{space 1}  -12.32{col 57}{space 3}0.000{col 65}{space 4}-.2221266{col 78}{space 3}-.1611556
{txt}{space 8}income2quartile {c |}{col 25}{res}{space 2} .0044154{col 37}{space 2}  .012891{col 48}{space 1}    0.34{col 57}{space 3}0.732{col 65}{space 4}-.0208519{col 78}{space 3} .0296826
{txt}{space 8}income3quartile {c |}{col 25}{res}{space 2} .0239857{col 37}{space 2} .0151597{col 48}{space 1}    1.58{col 57}{space 3}0.114{col 65}{space 4}-.0057285{col 78}{space 3} .0536998
{txt}{space 8}income4quartile {c |}{col 25}{res}{space 2}-.0001217{col 37}{space 2} .0232936{col 48}{space 1}   -0.01{col 57}{space 3}0.996{col 65}{space 4}-.0457787{col 78}{space 3} .0455354
{txt}{space 9}incomenoanswer {c |}{col 25}{res}{space 2}-.0101274{col 37}{space 2} .0159811{col 48}{space 1}   -0.63{col 57}{space 3}0.526{col 65}{space 4}-.0414515{col 78}{space 3} .0211966
{txt}{space 17}female {c |}{col 25}{res}{space 2}  .036732{col 37}{space 2} .0085111{col 48}{space 1}    4.32{col 57}{space 3}0.000{col 65}{space 4} .0200496{col 78}{space 3} .0534144
{txt}{space 13}highschool {c |}{col 25}{res}{space 2}-.0465504{col 37}{space 2} .0121187{col 48}{space 1}   -3.84{col 57}{space 3}0.000{col 65}{space 4} -.070304{col 78}{space 3}-.0227969
{txt}{space 16}college {c |}{col 25}{res}{space 2} -.039895{col 37}{space 2} .0105691{col 48}{space 1}   -3.77{col 57}{space 3}0.000{col 65}{space 4}-.0606112{col 78}{space 3}-.0191787
{txt}{space 13}noreligion {c |}{col 25}{res}{space 2}-.0302062{col 37}{space 2} .0208532{col 48}{space 1}   -1.45{col 57}{space 3}0.147{col 65}{space 4}-.0710799{col 78}{space 3} .0106675
{txt}{space 3}christiannotcatholic {c |}{col 25}{res}{space 2}-.0629687{col 37}{space 2} .0363169{col 48}{space 1}   -1.73{col 57}{space 3}0.083{col 65}{space 4}-.1341524{col 78}{space 3}  .008215
{txt}{space 15}catholic {c |}{col 25}{res}{space 2}-.0230153{col 37}{space 2} .0177797{col 48}{space 1}   -1.29{col 57}{space 3}0.196{col 65}{space 4}-.0578647{col 78}{space 3} .0118341
{txt}{space 9}fulltimeworker {c |}{col 25}{res}{space 2}-.0417867{col 37}{space 2} .1618089{col 48}{space 1}   -0.26{col 57}{space 3}0.796{col 65}{space 4}-.3589435{col 78}{space 3}   .27537
{txt}{space 9}parttimeworker {c |}{col 25}{res}{space 2}-.0674223{col 37}{space 2} .1613911{col 48}{space 1}   -0.42{col 57}{space 3}0.676{col 65}{space 4}-.3837601{col 78}{space 3} .2489155
{txt}{space 13}unemployed {c |}{col 25}{res}{space 2}-.0791859{col 37}{space 2} .1800862{col 48}{space 1}   -0.44{col 57}{space 3}0.660{col 65}{space 4}-.4321674{col 78}{space 3} .2737955
{txt}{space 11}selfemployed {c |}{col 25}{res}{space 2}-.0345888{col 37}{space 2} .1962274{col 48}{space 1}   -0.18{col 57}{space 3}0.860{col 65}{space 4}-.4192081{col 78}{space 3} .3500305
{txt}{space 16}outofLF {c |}{col 25}{res}{space 2} -.055925{col 37}{space 2} .1681443{col 48}{space 1}   -0.33{col 57}{space 3}0.739{col 65}{space 4}-.3854996{col 78}{space 3} .2736495
{txt}{space 13}goodhealth {c |}{col 25}{res}{space 2} -.007552{col 37}{space 2} .0267076{col 48}{space 1}   -0.28{col 57}{space 3}0.777{col 65}{space 4}-.0599009{col 78}{space 3} .0447968
{txt}{space 18}white {c |}{col 25}{res}{space 2} .0860945{col 37}{space 2} .0944427{col 48}{space 1}    0.91{col 57}{space 3}0.362{col 65}{space 4}-.0990198{col 78}{space 3} .2712088
{txt}{space 18}black {c |}{col 25}{res}{space 2} .1234015{col 37}{space 2} .0723289{col 48}{space 1}    1.71{col 57}{space 3}0.088{col 65}{space 4}-.0183683{col 78}{space 3} .2651712
{txt}{space 17}latino {c |}{col 25}{res}{space 2} .1641328{col 37}{space 2} .0790976{col 48}{space 1}    2.08{col 57}{space 3}0.038{col 65}{space 4} .0090961{col 78}{space 3} .3191696
{txt}{space 18}asian {c |}{col 25}{res}{space 2} .2247013{col 37}{space 2} .0846045{col 48}{space 1}    2.66{col 57}{space 3}0.008{col 65}{space 4} .0588706{col 78}{space 3} .3905321
{txt}{space 12}whitecollar {c |}{col 25}{res}{space 2} .0278236{col 37}{space 2} .0185765{col 48}{space 1}    1.50{col 57}{space 3}0.134{col 65}{space 4}-.0085876{col 78}{space 3} .0642349
{txt}{space 13}bluecollar {c |}{col 25}{res}{space 2} .0070589{col 37}{space 2} .0209692{col 48}{space 1}    0.34{col 57}{space 3}0.736{col 65}{space 4}-.0340422{col 78}{space 3}   .04816
{txt}{space 10}serviceworker {c |}{col 25}{res}{space 2}  .000253{col 37}{space 2} .0147277{col 48}{space 1}    0.02{col 57}{space 3}0.986{col 65}{space 4}-.0286143{col 78}{space 3} .0291203
{txt}{space 12}mv_thirties {c |}{col 25}{res}{space 2}        0{col 37}{txt}  (omitted)
{space 12}mv_fourties {c |}{col 25}{res}{space 2}        0{col 37}{txt}  (omitted)
{space 13}mv_fifties {c |}{col 25}{res}{space 2}        0{col 37}{txt}  (omitted)
{space 13}mv_sixties {c |}{col 25}{res}{space 2}        0{col 37}{txt}  (omitted)
{space 11}mv_seventies {c |}{col 25}{res}{space 2}        0{col 37}{txt}  (omitted)
{space 5}mv_income2quartile {c |}{col 25}{res}{space 2}        0{col 37}{txt}  (omitted)
{space 5}mv_income3quartile {c |}{col 25}{res}{space 2}        0{col 37}{txt}  (omitted)
{space 5}mv_income4quartile {c |}{col 25}{res}{space 2}        0{col 37}{txt}  (omitted)
{space 6}mv_incomenoanswer {c |}{col 25}{res}{space 2} .0712461{col 37}{space 2} .0440839{col 48}{space 1}    1.62{col 57}{space 3}0.106{col 65}{space 4}-.0151614{col 78}{space 3} .1576535
{txt}{space 14}mv_female {c |}{col 25}{res}{space 2}        0{col 37}{txt}  (omitted)
{space 10}mv_highschool {c |}{col 25}{res}{space 2}-.0456032{col 37}{space 2} .0277797{col 48}{space 1}   -1.64{col 57}{space 3}0.101{col 65}{space 4}-.1000534{col 78}{space 3}  .008847
{txt}{space 13}mv_college {c |}{col 25}{res}{space 2}        0{col 37}{txt}  (omitted)
{space 10}mv_noreligion {c |}{col 25}{res}{space 2} .0277018{col 37}{space 2} .0383372{col 48}{space 1}    0.72{col 57}{space 3}0.470{col 65}{space 4}-.0474417{col 78}{space 3} .1028453
{txt}mv_christiannotcatholic {c |}{col 25}{res}{space 2}        0{col 37}{txt}  (omitted)
{space 12}mv_catholic {c |}{col 25}{res}{space 2}        0{col 37}{txt}  (omitted)
{space 6}mv_fulltimeworker {c |}{col 25}{res}{space 2}        0{col 37}{txt}  (omitted)
{space 6}mv_parttimeworker {c |}{col 25}{res}{space 2}-.0594776{col 37}{space 2} .1518709{col 48}{space 1}   -0.39{col 57}{space 3}0.695{col 65}{space 4}-.3571551{col 78}{space 3} .2381998
{txt}{space 10}mv_unemployed {c |}{col 25}{res}{space 2}        0{col 37}{txt}  (omitted)
{space 8}mv_selfemployed {c |}{col 25}{res}{space 2} -.181619{col 37}{space 2} .1559453{col 48}{space 1}   -1.16{col 57}{space 3}0.244{col 65}{space 4}-.4872827{col 78}{space 3} .1240446
{txt}{space 13}mv_outofLF {c |}{col 25}{res}{space 2}        0{col 37}{txt}  (omitted)
{space 10}mv_goodhealth {c |}{col 25}{res}{space 2} .1893161{col 37}{space 2} .1489719{col 48}{space 1}    1.27{col 57}{space 3}0.204{col 65}{space 4}-.1026793{col 78}{space 3} .4813114
{txt}{space 15}mv_white {c |}{col 25}{res}{space 2} .1577808{col 37}{space 2} .2066798{col 48}{space 1}    0.76{col 57}{space 3}0.445{col 65}{space 4}-.2473259{col 78}{space 3} .5628875
{txt}{space 15}mv_black {c |}{col 25}{res}{space 2}        0{col 37}{txt}  (omitted)
{space 14}mv_latino {c |}{col 25}{res}{space 2}        0{col 37}{txt}  (omitted)
{space 15}mv_asian {c |}{col 25}{res}{space 2}        0{col 37}{txt}  (omitted)
{space 9}mv_whitecollar {c |}{col 25}{res}{space 2}-.0270247{col 37}{space 2} .1600531{col 48}{space 1}   -0.17{col 57}{space 3}0.866{col 65}{space 4}-.3407398{col 78}{space 3} .2866904
{txt}{space 10}mv_bluecollar {c |}{col 25}{res}{space 2}        0{col 37}{txt}  (omitted)
{space 7}mv_serviceworker {c |}{col 25}{res}{space 2}        0{col 37}{txt}  (omitted)
{space 23} {c |}
{space 16}country {c |}
{space 15}Austria  {c |}{col 25}{res}{space 2} .2383204{col 37}{space 2} .2748171{col 48}{space 1}    0.87{col 57}{space 3}0.386{col 65}{space 4}-.3003403{col 78}{space 3}  .776981
{txt}{space 16}Brazil  {c |}{col 25}{res}{space 2} .0148062{col 37}{space 2} .0348296{col 48}{space 1}    0.43{col 57}{space 3}0.671{col 65}{space 4}-.0534622{col 78}{space 3} .0830746
{txt}{space 16}France  {c |}{col 25}{res}{space 2}        0{col 37}{txt}  (omitted)
{space 15}Germany  {c |}{col 25}{res}{space 2} .0564759{col 37}{space 2} .2941691{col 48}{space 1}    0.19{col 57}{space 3}0.848{col 65}{space 4}-.5201158{col 78}{space 3} .6330677
{txt}{space 17}Italy  {c |}{col 25}{res}{space 2} .0451232{col 37}{space 2} .2650224{col 48}{space 1}    0.17{col 57}{space 3}0.865{col 65}{space 4}-.4743391{col 78}{space 3} .5645856
{txt}{space 11}New_Zealand  {c |}{col 25}{res}{space 2}        0{col 37}{txt}  (omitted)
{space 16}Poland  {c |}{col 25}{res}{space 2}-.0131223{col 37}{space 2} .0387657{col 48}{space 1}   -0.34{col 57}{space 3}0.735{col 65}{space 4}-.0891058{col 78}{space 3} .0628612
{txt}{space 17}Spain  {c |}{col 25}{res}{space 2}        0{col 37}{txt}  (omitted)
{space 16}Sweden  {c |}{col 25}{res}{space 2}-.1764496{col 37}{space 2} .0454001{col 48}{space 1}   -3.89{col 57}{space 3}0.000{col 65}{space 4}-.2654369{col 78}{space 3}-.0874623
{txt}{space 20}UK  {c |}{col 25}{res}{space 2}        0{col 37}{txt}  (omitted)
{space 20}US  {c |}{col 25}{res}{space 2}        0{col 37}{txt}  (omitted)
{space 23} {c |}
{space 18}_cons {c |}{col 25}{res}{space 2} .8491719{col 37}{space 2} .0759134{col 48}{space 1}   11.19{col 57}{space 3}0.000{col 65}{space 4} .7003764{col 78}{space 3} .9979673
{txt}{hline 24}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{help ivreg2##idtest:Underidentification test} (Kleibergen-Paap rk LM statistic):{res}{col 71}  10.097
{txt}{col 52}Chi-sq({res}1{txt}) P-val =  {res}{col 73}0.0015
{txt}{hline 78}
{help ivreg2##widtest:Weak identification test} (Cragg-Donald Wald F statistic):{res}{col 71}  10.062
{txt}                         (Kleibergen-Paap rk Wald F statistic):{res}{col 71}  10.092
{txt}Stock-Yogo weak ID test critical values:{res}{txt}{col 42}10% maximal IV size{res}{col 73} 16.38
{txt}{col 42}15% maximal IV size{res}{col 73}  8.96
{txt}{col 42}20% maximal IV size{res}{col 73}  6.66
{txt}{col 42}25% maximal IV size{res}{col 73}  5.53
{txt}Source: Stock-Yogo (2005).  Reproduced by permission.
NB: Critical values are for Cragg-Donald F statistic and i.i.d. errors.
{hline 78}
{help ivreg2##overidtests:Hansen J statistic} (overidentification test of all instruments):{res}{col 71}   0.000
{txt}{col 50}(equation exactly identified)
{hline 78}
Instrumented:{col 23}satis_head
Included instruments:{col 23}thirties fourties fifties sixties seventies
{col 23}income2quartile income3quartile income4quartile
{col 23}incomenoanswer female highschool college noreligion
{col 23}christiannotcatholic catholic fulltimeworker
{col 23}parttimeworker unemployed selfemployed outofLF goodhealth
{col 23}white black latino asian whitecollar bluecollar
{col 23}serviceworker mv_incomenoanswer mv_highschool
{col 23}mv_noreligion mv_parttimeworker mv_selfemployed
{col 23}mv_goodhealth mv_white mv_whitecollar 2.country 3.country
{col 23}5.country 6.country 8.country 10.country
Excluded instruments:{col 23}treatc
Dropped collinear:{col 23}mv_thirties mv_fourties mv_fifties mv_sixties
{col 23}mv_seventies mv_income2quartile mv_income3quartile
{col 23}mv_income4quartile mv_female mv_college
{col 23}mv_christiannotcatholic mv_catholic mv_fulltimeworker
{col 23}mv_unemployed mv_outofLF mv_black mv_latino mv_asian
{col 23}mv_bluecollar mv_serviceworker 4.country 7.country
{col 23}9.country 11.country 12.country
{hline 78}
({res}est4{txt} stored)

{com}. 
. estadd scalar fstat = e(widstat)

{txt}added scalar:
              e(fstat) =  {res}10.092037
{txt}
{com}. estadd local CFE "X", replace

{txt}added macro:
                e(CFE) : "{res:X}"

{com}. estadd local IV "SumIV", replace

{txt}added macro:
                 e(IV) : "{res:SumIV}"

{com}. estadd local Controls "X", replace

{txt}added macro:
           e(Controls) : "{res:X}"

{com}. get_mean

{txt}added scalar:
                 e(av) =  {res}.59
{txt}
{com}. 
. /* 4. Col 5: Army with 16 instruments*/ 
. * to get the Cragg-Donald statistic: 
. eststo: ivreg2 army ///
>         (satis_head = TH1_TE1 TH1_TE2 TH1_TE3 TH1_TE4 TH2_TE1 TH2_TE2 TH2_TE3 TH2_TE4 TH3_TE1 TH3_TE2 TH3_TE3 TH3_TE4 TH4_TE1 TH4_TE2 TH4_TE3) $controls $mv_controls  i.country, small robust 
{txt}Warning - collinearities detected
Vars dropped:{col 21}mv_thirties mv_fourties mv_fifties mv_sixties mv_seventies
{col 21}mv_income2quartile mv_income3quartile mv_income4quartile
{col 21}mv_female mv_college mv_christiannotcatholic mv_catholic
{col 21}mv_fulltimeworker mv_unemployed mv_outofLF mv_black
{col 21}mv_latino mv_asian mv_bluecollar mv_serviceworker 4.country
{col 21}7.country 9.country 11.country 12.country
{res}
{txt}IV (2SLS) estimation
{hline 20}

Estimates efficient for homoskedasticity only
Statistics robust to heteroskedasticity

{col 55}Number of obs = {res}   22536
{txt}{col 55}F( 43, 22492) = {res}   30.53
{txt}{col 55}Prob > F      = {res}  0.0000
{txt}Total (centered) SS     = {res}  3338.15118{txt}{col 55}Centered R2   = {res} -0.2087
{txt}Total (uncentered) SS   = {res}        4075{txt}{col 55}Uncentered R2 = {res}  0.0098
{txt}Residual SS             = {res} 4034.865301{txt}{col 55}Root MSE      = {res}   .4235

{txt}{hline 24}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 25}{c |}{col 37}    Robust
{col 1}                   army{col 25}{c |} Coefficient{col 37}  std. err.{col 49}      t{col 57}   P>|t|{col 65}     [95% con{col 78}f. interval]
{hline 24}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 13}satis_head {c |}{col 25}{res}{space 2}-.6014081{col 37}{space 2}  .294852{col 48}{space 1}   -2.04{col 57}{space 3}0.041{col 65}{space 4}-1.179338{col 78}{space 3}-.0234777
{txt}{space 15}thirties {c |}{col 25}{res}{space 2} .0095008{col 37}{space 2} .0122781{col 48}{space 1}    0.77{col 57}{space 3}0.439{col 65}{space 4}-.0145651{col 78}{space 3} .0335667
{txt}{space 15}fourties {c |}{col 25}{res}{space 2}-.0667485{col 37}{space 2} .0145436{col 48}{space 1}   -4.59{col 57}{space 3}0.000{col 65}{space 4} -.095255{col 78}{space 3} -.038242
{txt}{space 16}fifties {c |}{col 25}{res}{space 2}-.1402793{col 37}{space 2}  .014801{col 48}{space 1}   -9.48{col 57}{space 3}0.000{col 65}{space 4}-.1692903{col 78}{space 3}-.1112684
{txt}{space 16}sixties {c |}{col 25}{res}{space 2} -.182826{col 37}{space 2} .0150055{col 48}{space 1}  -12.18{col 57}{space 3}0.000{col 65}{space 4}-.2122378{col 78}{space 3}-.1534141
{txt}{space 14}seventies {c |}{col 25}{res}{space 2}-.1736625{col 37}{space 2} .0125234{col 48}{space 1}  -13.87{col 57}{space 3}0.000{col 65}{space 4}-.1982091{col 78}{space 3}-.1491158
{txt}{space 8}income2quartile {c |}{col 25}{res}{space 2}-.0132046{col 37}{space 2} .0099967{col 48}{space 1}   -1.32{col 57}{space 3}0.187{col 65}{space 4}-.0327988{col 78}{space 3} .0063897
{txt}{space 8}income3quartile {c |}{col 25}{res}{space 2}-.0098752{col 37}{space 2} .0110342{col 48}{space 1}   -0.89{col 57}{space 3}0.371{col 65}{space 4}-.0315029{col 78}{space 3} .0117526
{txt}{space 8}income4quartile {c |}{col 25}{res}{space 2}-.0168169{col 37}{space 2} .0150817{col 48}{space 1}   -1.12{col 57}{space 3}0.265{col 65}{space 4} -.046378{col 78}{space 3} .0127442
{txt}{space 9}incomenoanswer {c |}{col 25}{res}{space 2}-.0191017{col 37}{space 2} .0133338{col 48}{space 1}   -1.43{col 57}{space 3}0.152{col 65}{space 4}-.0452369{col 78}{space 3} .0070335
{txt}{space 17}female {c |}{col 25}{res}{space 2} .0063746{col 37}{space 2} .0065657{col 48}{space 1}    0.97{col 57}{space 3}0.332{col 65}{space 4}-.0064947{col 78}{space 3} .0192439
{txt}{space 13}highschool {c |}{col 25}{res}{space 2}-.0527549{col 37}{space 2} .0103665{col 48}{space 1}   -5.09{col 57}{space 3}0.000{col 65}{space 4}-.0730741{col 78}{space 3}-.0324358
{txt}{space 16}college {c |}{col 25}{res}{space 2}-.1001767{col 37}{space 2} .0092853{col 48}{space 1}  -10.79{col 57}{space 3}0.000{col 65}{space 4}-.1183766{col 78}{space 3}-.0819768
{txt}{space 13}noreligion {c |}{col 25}{res}{space 2}-.1328383{col 37}{space 2} .0169688{col 48}{space 1}   -7.83{col 57}{space 3}0.000{col 65}{space 4}-.1660982{col 78}{space 3}-.0995783
{txt}{space 3}christiannotcatholic {c |}{col 25}{res}{space 2}-.0128012{col 37}{space 2} .0243926{col 48}{space 1}   -0.52{col 57}{space 3}0.600{col 65}{space 4}-.0606125{col 78}{space 3}   .03501
{txt}{space 15}catholic {c |}{col 25}{res}{space 2}-.0253912{col 37}{space 2} .0157233{col 48}{space 1}   -1.61{col 57}{space 3}0.106{col 65}{space 4}  -.05621{col 78}{space 3} .0054276
{txt}{space 9}fulltimeworker {c |}{col 25}{res}{space 2}-.2054804{col 37}{space 2} .0936745{col 48}{space 1}   -2.19{col 57}{space 3}0.028{col 65}{space 4}-.3890889{col 78}{space 3}-.0218719
{txt}{space 9}parttimeworker {c |}{col 25}{res}{space 2}-.1848824{col 37}{space 2}  .097756{col 48}{space 1}   -1.89{col 57}{space 3}0.059{col 65}{space 4}-.3764909{col 78}{space 3} .0067261
{txt}{space 13}unemployed {c |}{col 25}{res}{space 2}-.2510777{col 37}{space 2} .1049285{col 48}{space 1}   -2.39{col 57}{space 3}0.017{col 65}{space 4} -.456745{col 78}{space 3}-.0454105
{txt}{space 11}selfemployed {c |}{col 25}{res}{space 2}-.2455276{col 37}{space 2} .1168258{col 48}{space 1}   -2.10{col 57}{space 3}0.036{col 65}{space 4}-.4745144{col 78}{space 3}-.0165409
{txt}{space 16}outofLF {c |}{col 25}{res}{space 2}-.2083613{col 37}{space 2} .0971584{col 48}{space 1}   -2.14{col 57}{space 3}0.032{col 65}{space 4}-.3987985{col 78}{space 3} -.017924
{txt}{space 13}goodhealth {c |}{col 25}{res}{space 2} .0294551{col 37}{space 2} .0159763{col 48}{space 1}    1.84{col 57}{space 3}0.065{col 65}{space 4}-.0018596{col 78}{space 3} .0607698
{txt}{space 18}white {c |}{col 25}{res}{space 2}  .201498{col 37}{space 2} .0678197{col 48}{space 1}    2.97{col 57}{space 3}0.003{col 65}{space 4} .0685667{col 78}{space 3} .3344294
{txt}{space 18}black {c |}{col 25}{res}{space 2} .2122019{col 37}{space 2} .0669805{col 48}{space 1}    3.17{col 57}{space 3}0.002{col 65}{space 4} .0809155{col 78}{space 3} .3434882
{txt}{space 17}latino {c |}{col 25}{res}{space 2} .2374429{col 37}{space 2} .0790049{col 48}{space 1}    3.01{col 57}{space 3}0.003{col 65}{space 4} .0825878{col 78}{space 3} .3922979
{txt}{space 18}asian {c |}{col 25}{res}{space 2} .2329384{col 37}{space 2} .0780154{col 48}{space 1}    2.99{col 57}{space 3}0.003{col 65}{space 4} .0800228{col 78}{space 3} .3858541
{txt}{space 12}whitecollar {c |}{col 25}{res}{space 2}-.0157013{col 37}{space 2} .0155834{col 48}{space 1}   -1.01{col 57}{space 3}0.314{col 65}{space 4}-.0462458{col 78}{space 3} .0148433
{txt}{space 13}bluecollar {c |}{col 25}{res}{space 2} .0090414{col 37}{space 2} .0172861{col 48}{space 1}    0.52{col 57}{space 3}0.601{col 65}{space 4}-.0248405{col 78}{space 3} .0429233
{txt}{space 10}serviceworker {c |}{col 25}{res}{space 2} .0178091{col 37}{space 2} .0128196{col 48}{space 1}    1.39{col 57}{space 3}0.165{col 65}{space 4}-.0073182{col 78}{space 3} .0429365
{txt}{space 12}mv_thirties {c |}{col 25}{res}{space 2}        0{col 37}{txt}  (omitted)
{space 12}mv_fourties {c |}{col 25}{res}{space 2}        0{col 37}{txt}  (omitted)
{space 13}mv_fifties {c |}{col 25}{res}{space 2}        0{col 37}{txt}  (omitted)
{space 13}mv_sixties {c |}{col 25}{res}{space 2}        0{col 37}{txt}  (omitted)
{space 11}mv_seventies {c |}{col 25}{res}{space 2}        0{col 37}{txt}  (omitted)
{space 5}mv_income2quartile {c |}{col 25}{res}{space 2}        0{col 37}{txt}  (omitted)
{space 5}mv_income3quartile {c |}{col 25}{res}{space 2}        0{col 37}{txt}  (omitted)
{space 5}mv_income4quartile {c |}{col 25}{res}{space 2}        0{col 37}{txt}  (omitted)
{space 6}mv_incomenoanswer {c |}{col 25}{res}{space 2} .0874042{col 37}{space 2} .0303484{col 48}{space 1}    2.88{col 57}{space 3}0.004{col 65}{space 4} .0279193{col 78}{space 3} .1468891
{txt}{space 14}mv_female {c |}{col 25}{res}{space 2}        0{col 37}{txt}  (omitted)
{space 10}mv_highschool {c |}{col 25}{res}{space 2} -.007448{col 37}{space 2}  .023558{col 48}{space 1}   -0.32{col 57}{space 3}0.752{col 65}{space 4}-.0536233{col 78}{space 3} .0387272
{txt}{space 13}mv_college {c |}{col 25}{res}{space 2}        0{col 37}{txt}  (omitted)
{space 10}mv_noreligion {c |}{col 25}{res}{space 2} -.033892{col 37}{space 2} .0405061{col 48}{space 1}   -0.84{col 57}{space 3}0.403{col 65}{space 4}-.1132867{col 78}{space 3} .0455027
{txt}mv_christiannotcatholic {c |}{col 25}{res}{space 2}        0{col 37}{txt}  (omitted)
{space 12}mv_catholic {c |}{col 25}{res}{space 2}        0{col 37}{txt}  (omitted)
{space 6}mv_fulltimeworker {c |}{col 25}{res}{space 2}        0{col 37}{txt}  (omitted)
{space 6}mv_parttimeworker {c |}{col 25}{res}{space 2}-.1520396{col 37}{space 2} .0905725{col 48}{space 1}   -1.68{col 57}{space 3}0.093{col 65}{space 4}-.3295679{col 78}{space 3} .0254887
{txt}{space 10}mv_unemployed {c |}{col 25}{res}{space 2}        0{col 37}{txt}  (omitted)
{space 8}mv_selfemployed {c |}{col 25}{res}{space 2} .1762104{col 37}{space 2} .0930935{col 48}{space 1}    1.89{col 57}{space 3}0.058{col 65}{space 4}-.0062594{col 78}{space 3} .3586801
{txt}{space 13}mv_outofLF {c |}{col 25}{res}{space 2}        0{col 37}{txt}  (omitted)
{space 10}mv_goodhealth {c |}{col 25}{res}{space 2} .4553235{col 37}{space 2} .1574222{col 48}{space 1}    2.89{col 57}{space 3}0.004{col 65}{space 4} .1467651{col 78}{space 3} .7638819
{txt}{space 15}mv_white {c |}{col 25}{res}{space 2} .4323949{col 37}{space 2} .1249668{col 48}{space 1}    3.46{col 57}{space 3}0.001{col 65}{space 4} .1874514{col 78}{space 3} .6773385
{txt}{space 15}mv_black {c |}{col 25}{res}{space 2}        0{col 37}{txt}  (omitted)
{space 14}mv_latino {c |}{col 25}{res}{space 2}        0{col 37}{txt}  (omitted)
{space 15}mv_asian {c |}{col 25}{res}{space 2}        0{col 37}{txt}  (omitted)
{space 9}mv_whitecollar {c |}{col 25}{res}{space 2} .2909659{col 37}{space 2} .0951142{col 48}{space 1}    3.06{col 57}{space 3}0.002{col 65}{space 4} .1045353{col 78}{space 3} .4773964
{txt}{space 10}mv_bluecollar {c |}{col 25}{res}{space 2}        0{col 37}{txt}  (omitted)
{space 7}mv_serviceworker {c |}{col 25}{res}{space 2}        0{col 37}{txt}  (omitted)
{space 23} {c |}
{space 16}country {c |}
{space 15}Austria  {c |}{col 25}{res}{space 2} .4996815{col 37}{space 2} .1637394{col 48}{space 1}    3.05{col 57}{space 3}0.002{col 65}{space 4} .1787408{col 78}{space 3} .8206222
{txt}{space 16}Brazil  {c |}{col 25}{res}{space 2} .2586537{col 37}{space 2} .0331342{col 48}{space 1}    7.81{col 57}{space 3}0.000{col 65}{space 4} .1937082{col 78}{space 3} .3235991
{txt}{space 16}France  {c |}{col 25}{res}{space 2}        0{col 37}{txt}  (omitted)
{space 15}Germany  {c |}{col 25}{res}{space 2} .5828578{col 37}{space 2} .1745989{col 48}{space 1}    3.34{col 57}{space 3}0.001{col 65}{space 4} .2406319{col 78}{space 3} .9250838
{txt}{space 17}Italy  {c |}{col 25}{res}{space 2} .5899371{col 37}{space 2} .1614414{col 48}{space 1}    3.65{col 57}{space 3}0.000{col 65}{space 4} .2735007{col 78}{space 3} .9063734
{txt}{space 11}New_Zealand  {c |}{col 25}{res}{space 2}        0{col 37}{txt}  (omitted)
{space 16}Poland  {c |}{col 25}{res}{space 2} .0945012{col 37}{space 2} .0325401{col 48}{space 1}    2.90{col 57}{space 3}0.004{col 65}{space 4} .0307202{col 78}{space 3} .1582821
{txt}{space 17}Spain  {c |}{col 25}{res}{space 2}        0{col 37}{txt}  (omitted)
{space 16}Sweden  {c |}{col 25}{res}{space 2} .1496322{col 37}{space 2} .0337026{col 48}{space 1}    4.44{col 57}{space 3}0.000{col 65}{space 4} .0835727{col 78}{space 3} .2156917
{txt}{space 20}UK  {c |}{col 25}{res}{space 2}        0{col 37}{txt}  (omitted)
{space 20}US  {c |}{col 25}{res}{space 2}        0{col 37}{txt}  (omitted)
{space 23} {c |}
{space 18}_cons {c |}{col 25}{res}{space 2}  .079623{col 37}{space 2} .0661125{col 48}{space 1}    1.20{col 57}{space 3}0.228{col 65}{space 4}-.0499622{col 78}{space 3} .2092082
{txt}{hline 24}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{help ivreg2##idtest:Underidentification test} (Kleibergen-Paap rk LM statistic):{res}{col 71}  23.447
{txt}{col 52}Chi-sq({res}15{txt}) P-val =  {res}{col 73}0.0751
{txt}{hline 78}
{help ivreg2##widtest:Weak identification test} (Cragg-Donald Wald F statistic):{res}{col 71}   1.535
{txt}                         (Kleibergen-Paap rk Wald F statistic):{res}{col 71}   1.566
{txt}Stock-Yogo weak ID test critical values:{res}{txt}{col 42} 5% maximal IV relative bias{res}{col 73} 21.23
{txt}{col 42}10% maximal IV relative bias{res}{col 73} 11.51
{txt}{col 42}20% maximal IV relative bias{res}{col 73}  6.42
{txt}{col 42}30% maximal IV relative bias{res}{col 73}  4.63
{txt}{col 42}10% maximal IV size{res}{col 73} 50.39
{txt}{col 42}15% maximal IV size{res}{col 73} 26.80
{txt}{col 42}20% maximal IV size{res}{col 73} 18.72
{txt}{col 42}25% maximal IV size{res}{col 73} 14.60
{txt}Source: Stock-Yogo (2005).  Reproduced by permission.
NB: Critical values are for Cragg-Donald F statistic and i.i.d. errors.
{hline 78}
{help ivreg2##overidtests:Hansen J statistic} (overidentification test of all instruments):{res}{col 71}  14.882
{txt}{col 52}Chi-sq({res}14{txt}) P-val =  {res}{col 73}0.3863
{txt}{hline 78}
Instrumented:{col 23}satis_head
Included instruments:{col 23}thirties fourties fifties sixties seventies
{col 23}income2quartile income3quartile income4quartile
{col 23}incomenoanswer female highschool college noreligion
{col 23}christiannotcatholic catholic fulltimeworker
{col 23}parttimeworker unemployed selfemployed outofLF goodhealth
{col 23}white black latino asian whitecollar bluecollar
{col 23}serviceworker mv_incomenoanswer mv_highschool
{col 23}mv_noreligion mv_parttimeworker mv_selfemployed
{col 23}mv_goodhealth mv_white mv_whitecollar 2.country 3.country
{col 23}5.country 6.country 8.country 10.country
Excluded instruments:{col 23}TH1_TE1 TH1_TE2 TH1_TE3 TH1_TE4 TH2_TE1 TH2_TE2 TH2_TE3
{col 23}TH2_TE4 TH3_TE1 TH3_TE2 TH3_TE3 TH3_TE4 TH4_TE1 TH4_TE2
{col 23}TH4_TE3
Dropped collinear:{col 23}mv_thirties mv_fourties mv_fifties mv_sixties
{col 23}mv_seventies mv_income2quartile mv_income3quartile
{col 23}mv_income4quartile mv_female mv_college
{col 23}mv_christiannotcatholic mv_catholic mv_fulltimeworker
{col 23}mv_unemployed mv_outofLF mv_black mv_latino mv_asian
{col 23}mv_bluecollar mv_serviceworker 4.country 7.country
{col 23}9.country 11.country 12.country
{hline 78}
({res}est5{txt} stored)

{com}. 
. estadd scalar fstat = e(widstat)

{txt}added scalar:
              e(fstat) =  {res}1.5656641
{txt}
{com}. estadd local CFE "X", replace

{txt}added macro:
                e(CFE) : "{res:X}"

{com}. estadd local IV "16 IVs", replace

{txt}added macro:
                 e(IV) : "{res:16 IVs}"

{com}. estadd local Controls "X", replace

{txt}added macro:
           e(Controls) : "{res:X}"

{com}. get_mean

{txt}added scalar:
                 e(av) =  {res}.181
{txt}
{com}. 
. /* 4. Col 6: Army with 1 instrument*/   
. * to get the Cragg-Donald statistic: 
. eststo: ivreg2 army ///
>         (satis_head = treatc) $controls $mv_controls  i.country, small robust 
{txt}Warning - collinearities detected
Vars dropped:{col 21}mv_thirties mv_fourties mv_fifties mv_sixties mv_seventies
{col 21}mv_income2quartile mv_income3quartile mv_income4quartile
{col 21}mv_female mv_college mv_christiannotcatholic mv_catholic
{col 21}mv_fulltimeworker mv_unemployed mv_outofLF mv_black
{col 21}mv_latino mv_asian mv_bluecollar mv_serviceworker 4.country
{col 21}7.country 9.country 11.country 12.country
{res}
{txt}IV (2SLS) estimation
{hline 20}

Estimates efficient for homoskedasticity only
Statistics robust to heteroskedasticity

{col 55}Number of obs = {res}   22536
{txt}{col 55}F( 43, 22492) = {res}   29.20
{txt}{col 55}Prob > F      = {res}  0.0000
{txt}Total (centered) SS     = {res}  3338.15118{txt}{col 55}Centered R2   = {res} -0.2572
{txt}Total (uncentered) SS   = {res}        4075{txt}{col 55}Uncentered R2 = {res} -0.0299
{txt}Residual SS             = {res} 4196.713408{txt}{col 55}Root MSE      = {res}    .432

{txt}{hline 24}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 25}{c |}{col 37}    Robust
{col 1}                   army{col 25}{c |} Coefficient{col 37}  std. err.{col 49}      t{col 57}   P>|t|{col 65}     [95% con{col 78}f. interval]
{hline 24}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 13}satis_head {c |}{col 25}{res}{space 2}-.6574919{col 37}{space 2}  .462926{col 48}{space 1}   -1.42{col 57}{space 3}0.156{col 65}{space 4}-1.564859{col 78}{space 3} .2498753
{txt}{space 15}thirties {c |}{col 25}{res}{space 2} .0087549{col 37}{space 2} .0133493{col 48}{space 1}    0.66{col 57}{space 3}0.512{col 65}{space 4}-.0174107{col 78}{space 3} .0349204
{txt}{space 15}fourties {c |}{col 25}{res}{space 2}-.0685265{col 37}{space 2} .0185851{col 48}{space 1}   -3.69{col 57}{space 3}0.000{col 65}{space 4}-.1049547{col 78}{space 3}-.0320984
{txt}{space 16}fifties {c |}{col 25}{res}{space 2}-.1421732{col 37}{space 2} .0191444{col 48}{space 1}   -7.43{col 57}{space 3}0.000{col 65}{space 4}-.1796975{col 78}{space 3}-.1046489
{txt}{space 16}sixties {c |}{col 25}{res}{space 2}-.1848276{col 37}{space 2} .0197534{col 48}{space 1}   -9.36{col 57}{space 3}0.000{col 65}{space 4}-.2235457{col 78}{space 3}-.1461095
{txt}{space 14}seventies {c |}{col 25}{res}{space 2}-.1744151{col 37}{space 2} .0136276{col 48}{space 1}  -12.80{col 57}{space 3}0.000{col 65}{space 4}-.2011261{col 78}{space 3}-.1477041
{txt}{space 8}income2quartile {c |}{col 25}{res}{space 2}-.0122477{col 37}{space 2} .0117515{col 48}{space 1}   -1.04{col 57}{space 3}0.297{col 65}{space 4}-.0352815{col 78}{space 3}  .010786
{txt}{space 8}income3quartile {c |}{col 25}{res}{space 2}-.0086069{col 37}{space 2} .0137096{col 48}{space 1}   -0.63{col 57}{space 3}0.530{col 65}{space 4}-.0354788{col 78}{space 3} .0182649
{txt}{space 8}income4quartile {c |}{col 25}{res}{space 2}-.0145076{col 37}{space 2} .0210347{col 48}{space 1}   -0.69{col 57}{space 3}0.490{col 65}{space 4}-.0557371{col 78}{space 3} .0267219
{txt}{space 9}incomenoanswer {c |}{col 25}{res}{space 2}-.0196742{col 37}{space 2} .0140846{col 48}{space 1}   -1.40{col 57}{space 3}0.162{col 65}{space 4} -.047281{col 78}{space 3} .0079326
{txt}{space 17}female {c |}{col 25}{res}{space 2} .0069748{col 37}{space 2} .0076046{col 48}{space 1}    0.92{col 57}{space 3}0.359{col 65}{space 4}-.0079308{col 78}{space 3} .0218803
{txt}{space 13}highschool {c |}{col 25}{res}{space 2}-.0522212{col 37}{space 2} .0110315{col 48}{space 1}   -4.73{col 57}{space 3}0.000{col 65}{space 4}-.0738438{col 78}{space 3}-.0305987
{txt}{space 16}college {c |}{col 25}{res}{space 2}-.1000296{col 37}{space 2} .0094772{col 48}{space 1}  -10.55{col 57}{space 3}0.000{col 65}{space 4}-.1186056{col 78}{space 3}-.0814536
{txt}{space 13}noreligion {c |}{col 25}{res}{space 2}-.1345725{col 37}{space 2} .0204406{col 48}{space 1}   -6.58{col 57}{space 3}0.000{col 65}{space 4}-.1746374{col 78}{space 3}-.0945076
{txt}{space 3}christiannotcatholic {c |}{col 25}{res}{space 2}-.0092138{col 37}{space 2} .0335475{col 48}{space 1}   -0.27{col 57}{space 3}0.784{col 65}{space 4}-.0749691{col 78}{space 3} .0565416
{txt}{space 15}catholic {c |}{col 25}{res}{space 2} -.024177{col 37}{space 2}  .017742{col 48}{space 1}   -1.36{col 57}{space 3}0.173{col 65}{space 4}-.0589526{col 78}{space 3} .0105987
{txt}{space 9}fulltimeworker {c |}{col 25}{res}{space 2}  -.22304{col 37}{space 2} .1456627{col 48}{space 1}   -1.53{col 57}{space 3}0.126{col 65}{space 4}-.5085491{col 78}{space 3}  .062469
{txt}{space 9}parttimeworker {c |}{col 25}{res}{space 2}-.2020859{col 37}{space 2} .1467799{col 48}{space 1}   -1.38{col 57}{space 3}0.169{col 65}{space 4}-.4897846{col 78}{space 3} .0856128
{txt}{space 13}unemployed {c |}{col 25}{res}{space 2}-.2705293{col 37}{space 2} .1622155{col 48}{space 1}   -1.67{col 57}{space 3}0.095{col 65}{space 4}-.5884829{col 78}{space 3} .0474243
{txt}{space 11}selfemployed {c |}{col 25}{res}{space 2}-.2662817{col 37}{space 2} .1767422{col 48}{space 1}   -1.51{col 57}{space 3}0.132{col 65}{space 4}-.6127086{col 78}{space 3} .0801452
{txt}{space 16}outofLF {c |}{col 25}{res}{space 2}-.2266105{col 37}{space 2} .1512555{col 48}{space 1}   -1.50{col 57}{space 3}0.134{col 65}{space 4}-.5230818{col 78}{space 3} .0698608
{txt}{space 13}goodhealth {c |}{col 25}{res}{space 2} .0322629{col 37}{space 2} .0240147{col 48}{space 1}    1.34{col 57}{space 3}0.179{col 65}{space 4}-.0148076{col 78}{space 3} .0793335
{txt}{space 18}white {c |}{col 25}{res}{space 2} .2088833{col 37}{space 2} .0830961{col 48}{space 1}    2.51{col 57}{space 3}0.012{col 65}{space 4} .0460091{col 78}{space 3} .3717575
{txt}{space 18}black {c |}{col 25}{res}{space 2} .2128106{col 37}{space 2} .0686448{col 48}{space 1}    3.10{col 57}{space 3}0.002{col 65}{space 4} .0782621{col 78}{space 3} .3473591
{txt}{space 17}latino {c |}{col 25}{res}{space 2} .2394181{col 37}{space 2} .0816992{col 48}{space 1}    2.93{col 57}{space 3}0.003{col 65}{space 4} .0792821{col 78}{space 3} .3995541
{txt}{space 18}asian {c |}{col 25}{res}{space 2} .2370147{col 37}{space 2} .0835839{col 48}{space 1}    2.84{col 57}{space 3}0.005{col 65}{space 4} .0731845{col 78}{space 3} .4008448
{txt}{space 12}whitecollar {c |}{col 25}{res}{space 2} -.016659{col 37}{space 2} .0170149{col 48}{space 1}   -0.98{col 57}{space 3}0.328{col 65}{space 4}-.0500094{col 78}{space 3} .0166914
{txt}{space 13}bluecollar {c |}{col 25}{res}{space 2}  .007729{col 37}{space 2} .0194894{col 48}{space 1}    0.40{col 57}{space 3}0.692{col 65}{space 4}-.0304716{col 78}{space 3} .0459296
{txt}{space 10}serviceworker {c |}{col 25}{res}{space 2} .0172689{col 37}{space 2} .0135236{col 48}{space 1}    1.28{col 57}{space 3}0.202{col 65}{space 4}-.0092383{col 78}{space 3} .0437761
{txt}{space 12}mv_thirties {c |}{col 25}{res}{space 2}        0{col 37}{txt}  (omitted)
{space 12}mv_fourties {c |}{col 25}{res}{space 2}        0{col 37}{txt}  (omitted)
{space 13}mv_fifties {c |}{col 25}{res}{space 2}        0{col 37}{txt}  (omitted)
{space 13}mv_sixties {c |}{col 25}{res}{space 2}        0{col 37}{txt}  (omitted)
{space 11}mv_seventies {c |}{col 25}{res}{space 2}        0{col 37}{txt}  (omitted)
{space 5}mv_income2quartile {c |}{col 25}{res}{space 2}        0{col 37}{txt}  (omitted)
{space 5}mv_income3quartile {c |}{col 25}{res}{space 2}        0{col 37}{txt}  (omitted)
{space 5}mv_income4quartile {c |}{col 25}{res}{space 2}        0{col 37}{txt}  (omitted)
{space 6}mv_incomenoanswer {c |}{col 25}{res}{space 2}  .083202{col 37}{space 2} .0404884{col 48}{space 1}    2.05{col 57}{space 3}0.040{col 65}{space 4} .0038419{col 78}{space 3}  .162562
{txt}{space 14}mv_female {c |}{col 25}{res}{space 2}        0{col 37}{txt}  (omitted)
{space 10}mv_highschool {c |}{col 25}{res}{space 2}-.0080549{col 37}{space 2} .0241808{col 48}{space 1}   -0.33{col 57}{space 3}0.739{col 65}{space 4}-.0554509{col 78}{space 3}  .039341
{txt}{space 13}mv_college {c |}{col 25}{res}{space 2}        0{col 37}{txt}  (omitted)
{space 10}mv_noreligion {c |}{col 25}{res}{space 2}-.0336204{col 37}{space 2} .0411524{col 48}{space 1}   -0.82{col 57}{space 3}0.414{col 65}{space 4}-.1142819{col 78}{space 3} .0470411
{txt}mv_christiannotcatholic {c |}{col 25}{res}{space 2}        0{col 37}{txt}  (omitted)
{space 12}mv_catholic {c |}{col 25}{res}{space 2}        0{col 37}{txt}  (omitted)
{space 6}mv_fulltimeworker {c |}{col 25}{res}{space 2}        0{col 37}{txt}  (omitted)
{space 6}mv_parttimeworker {c |}{col 25}{res}{space 2}-.1682894{col 37}{space 2} .1374701{col 48}{space 1}   -1.22{col 57}{space 3}0.221{col 65}{space 4}-.4377402{col 78}{space 3} .1011615
{txt}{space 10}mv_unemployed {c |}{col 25}{res}{space 2}        0{col 37}{txt}  (omitted)
{space 8}mv_selfemployed {c |}{col 25}{res}{space 2} .1928931{col 37}{space 2} .1411461{col 48}{space 1}    1.37{col 57}{space 3}0.172{col 65}{space 4} -.083763{col 78}{space 3} .4695492
{txt}{space 13}mv_outofLF {c |}{col 25}{res}{space 2}        0{col 37}{txt}  (omitted)
{space 10}mv_goodhealth {c |}{col 25}{res}{space 2} .4652746{col 37}{space 2} .1706339{col 48}{space 1}    2.73{col 57}{space 3}0.006{col 65}{space 4} .1308203{col 78}{space 3} .7997288
{txt}{space 15}mv_white {c |}{col 25}{res}{space 2} .4537531{col 37}{space 2} .1845249{col 48}{space 1}    2.46{col 57}{space 3}0.014{col 65}{space 4} .0920714{col 78}{space 3} .8154347
{txt}{space 15}mv_black {c |}{col 25}{res}{space 2}        0{col 37}{txt}  (omitted)
{space 14}mv_latino {c |}{col 25}{res}{space 2}        0{col 37}{txt}  (omitted)
{space 15}mv_asian {c |}{col 25}{res}{space 2}        0{col 37}{txt}  (omitted)
{space 9}mv_whitecollar {c |}{col 25}{res}{space 2} .3080375{col 37}{space 2} .1445901{col 48}{space 1}    2.13{col 57}{space 3}0.033{col 65}{space 4} .0246309{col 78}{space 3} .5914441
{txt}{space 10}mv_bluecollar {c |}{col 25}{res}{space 2}        0{col 37}{txt}  (omitted)
{space 7}mv_serviceworker {c |}{col 25}{res}{space 2}        0{col 37}{txt}  (omitted)
{space 23} {c |}
{space 16}country {c |}
{space 15}Austria  {c |}{col 25}{res}{space 2} .5285666{col 37}{space 2}  .246115{col 48}{space 1}    2.15{col 57}{space 3}0.032{col 65}{space 4} .0461641{col 78}{space 3} 1.010969
{txt}{space 16}Brazil  {c |}{col 25}{res}{space 2} .2577514{col 37}{space 2} .0341264{col 48}{space 1}    7.55{col 57}{space 3}0.000{col 65}{space 4} .1908613{col 78}{space 3} .3246414
{txt}{space 16}France  {c |}{col 25}{res}{space 2}        0{col 37}{txt}  (omitted)
{space 15}Germany  {c |}{col 25}{res}{space 2} .6139337{col 37}{space 2} .2638367{col 48}{space 1}    2.33{col 57}{space 3}0.020{col 65}{space 4} .0967955{col 78}{space 3} 1.131072
{txt}{space 17}Italy  {c |}{col 25}{res}{space 2} .6174063{col 37}{space 2}  .237705{col 48}{space 1}    2.60{col 57}{space 3}0.009{col 65}{space 4} .1514879{col 78}{space 3} 1.083325
{txt}{space 11}New_Zealand  {c |}{col 25}{res}{space 2}        0{col 37}{txt}  (omitted)
{space 16}Poland  {c |}{col 25}{res}{space 2} .0925655{col 37}{space 2} .0351087{col 48}{space 1}    2.64{col 57}{space 3}0.008{col 65}{space 4} .0237501{col 78}{space 3} .1613809
{txt}{space 17}Spain  {c |}{col 25}{res}{space 2}        0{col 37}{txt}  (omitted)
{space 16}Sweden  {c |}{col 25}{res}{space 2} .1529184{col 37}{space 2} .0402747{col 48}{space 1}    3.80{col 57}{space 3}0.000{col 65}{space 4} .0739773{col 78}{space 3} .2318595
{txt}{space 20}UK  {c |}{col 25}{res}{space 2}        0{col 37}{txt}  (omitted)
{space 20}US  {c |}{col 25}{res}{space 2}        0{col 37}{txt}  (omitted)
{space 23} {c |}
{space 18}_cons {c |}{col 25}{res}{space 2} .0791559{col 37}{space 2} .0676273{col 48}{space 1}    1.17{col 57}{space 3}0.242{col 65}{space 4}-.0533983{col 78}{space 3}   .21171
{txt}{hline 24}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{help ivreg2##idtest:Underidentification test} (Kleibergen-Paap rk LM statistic):{res}{col 71}  10.020
{txt}{col 52}Chi-sq({res}1{txt}) P-val =  {res}{col 73}0.0015
{txt}{hline 78}
{help ivreg2##widtest:Weak identification test} (Cragg-Donald Wald F statistic):{res}{col 71}   9.985
{txt}                         (Kleibergen-Paap rk Wald F statistic):{res}{col 71}  10.016
{txt}Stock-Yogo weak ID test critical values:{res}{txt}{col 42}10% maximal IV size{res}{col 73} 16.38
{txt}{col 42}15% maximal IV size{res}{col 73}  8.96
{txt}{col 42}20% maximal IV size{res}{col 73}  6.66
{txt}{col 42}25% maximal IV size{res}{col 73}  5.53
{txt}Source: Stock-Yogo (2005).  Reproduced by permission.
NB: Critical values are for Cragg-Donald F statistic and i.i.d. errors.
{hline 78}
{help ivreg2##overidtests:Hansen J statistic} (overidentification test of all instruments):{res}{col 71}   0.000
{txt}{col 50}(equation exactly identified)
{hline 78}
Instrumented:{col 23}satis_head
Included instruments:{col 23}thirties fourties fifties sixties seventies
{col 23}income2quartile income3quartile income4quartile
{col 23}incomenoanswer female highschool college noreligion
{col 23}christiannotcatholic catholic fulltimeworker
{col 23}parttimeworker unemployed selfemployed outofLF goodhealth
{col 23}white black latino asian whitecollar bluecollar
{col 23}serviceworker mv_incomenoanswer mv_highschool
{col 23}mv_noreligion mv_parttimeworker mv_selfemployed
{col 23}mv_goodhealth mv_white mv_whitecollar 2.country 3.country
{col 23}5.country 6.country 8.country 10.country
Excluded instruments:{col 23}treatc
Dropped collinear:{col 23}mv_thirties mv_fourties mv_fifties mv_sixties
{col 23}mv_seventies mv_income2quartile mv_income3quartile
{col 23}mv_income4quartile mv_female mv_college
{col 23}mv_christiannotcatholic mv_catholic mv_fulltimeworker
{col 23}mv_unemployed mv_outofLF mv_black mv_latino mv_asian
{col 23}mv_bluecollar mv_serviceworker 4.country 7.country
{col 23}9.country 11.country 12.country
{hline 78}
({res}est6{txt} stored)

{com}. 
. estadd scalar fstat = e(widstat)

{txt}added scalar:
              e(fstat) =  {res}10.015576
{txt}
{com}. estadd local CFE "X", replace

{txt}added macro:
                e(CFE) : "{res:X}"

{com}. estadd local IV "SumIV", replace

{txt}added macro:
                 e(IV) : "{res:SumIV}"

{com}. estadd local Controls "X", replace

{txt}added macro:
           e(Controls) : "{res:X}"

{com}. get_mean

{txt}added scalar:
                 e(av) =  {res}.181
{txt}
{com}. 
. /* 4. Col 7: Democracy with 16 instruments*/    
. * to get the Cragg-Donald statistic: 
. eststo: ivreg2 democ ///
>         (satis_head = TH1_TE1 TH1_TE2 TH1_TE3 TH1_TE4 TH2_TE1 TH2_TE2 TH2_TE3 TH2_TE4 TH3_TE1 TH3_TE2 TH3_TE3 TH3_TE4 TH4_TE1 TH4_TE2 TH4_TE3) $controls $mv_controls  i.country, small robust 
{txt}Warning - collinearities detected
Vars dropped:{col 21}mv_thirties mv_fourties mv_fifties mv_sixties mv_seventies
{col 21}mv_income2quartile mv_income3quartile mv_income4quartile
{col 21}mv_female mv_college mv_christiannotcatholic mv_catholic
{col 21}mv_fulltimeworker mv_unemployed mv_outofLF mv_black
{col 21}mv_latino mv_asian mv_bluecollar mv_serviceworker 4.country
{col 21}7.country 9.country 11.country 12.country
{res}
{txt}IV (2SLS) estimation
{hline 20}

Estimates efficient for homoskedasticity only
Statistics robust to heteroskedasticity

{col 55}Number of obs = {res}   22537
{txt}{col 55}F( 43, 22493) = {res}   18.80
{txt}{col 55}Prob > F      = {res}  0.0000
{txt}Total (centered) SS     = {res} 1987.655944{txt}{col 55}Centered R2   = {res}  0.0392
{txt}Total (uncentered) SS   = {res}       20334{txt}{col 55}Uncentered R2 = {res}  0.9061
{txt}Residual SS             = {res} 1909.785059{txt}{col 55}Root MSE      = {res}   .2914

{txt}{hline 24}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 25}{c |}{col 37}    Robust
{col 1}                  democ{col 25}{c |} Coefficient{col 37}  std. err.{col 49}      t{col 57}   P>|t|{col 65}     [95% con{col 78}f. interval]
{hline 24}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 13}satis_head {c |}{col 25}{res}{space 2} .1023982{col 37}{space 2} .2083611{col 48}{space 1}    0.49{col 57}{space 3}0.623{col 65}{space 4} -.306004{col 78}{space 3} .5108004
{txt}{space 15}thirties {c |}{col 25}{res}{space 2} .0060911{col 37}{space 2} .0083963{col 48}{space 1}    0.73{col 57}{space 3}0.468{col 65}{space 4}-.0103662{col 78}{space 3} .0225484
{txt}{space 15}fourties {c |}{col 25}{res}{space 2} .0163058{col 37}{space 2}  .010126{col 48}{space 1}    1.61{col 57}{space 3}0.107{col 65}{space 4}-.0035418{col 78}{space 3} .0361535
{txt}{space 16}fifties {c |}{col 25}{res}{space 2} .0462606{col 37}{space 2}  .010371{col 48}{space 1}    4.46{col 57}{space 3}0.000{col 65}{space 4} .0259328{col 78}{space 3} .0665884
{txt}{space 16}sixties {c |}{col 25}{res}{space 2} .0689589{col 37}{space 2} .0105067{col 48}{space 1}    6.56{col 57}{space 3}0.000{col 65}{space 4}  .048365{col 78}{space 3} .0895528
{txt}{space 14}seventies {c |}{col 25}{res}{space 2} .0840891{col 37}{space 2} .0085161{col 48}{space 1}    9.87{col 57}{space 3}0.000{col 65}{space 4} .0673969{col 78}{space 3} .1007813
{txt}{space 8}income2quartile {c |}{col 25}{res}{space 2} .0281968{col 37}{space 2} .0071727{col 48}{space 1}    3.93{col 57}{space 3}0.000{col 65}{space 4} .0141378{col 78}{space 3} .0422558
{txt}{space 8}income3quartile {c |}{col 25}{res}{space 2} .0469364{col 37}{space 2} .0077142{col 48}{space 1}    6.08{col 57}{space 3}0.000{col 65}{space 4} .0318161{col 78}{space 3} .0620567
{txt}{space 8}income4quartile {c |}{col 25}{res}{space 2} .0509931{col 37}{space 2} .0103655{col 48}{space 1}    4.92{col 57}{space 3}0.000{col 65}{space 4}  .030676{col 78}{space 3} .0713102
{txt}{space 9}incomenoanswer {c |}{col 25}{res}{space 2} .0085015{col 37}{space 2} .0098195{col 48}{space 1}    0.87{col 57}{space 3}0.387{col 65}{space 4}-.0107453{col 78}{space 3} .0277484
{txt}{space 17}female {c |}{col 25}{res}{space 2}-.0101964{col 37}{space 2} .0045093{col 48}{space 1}   -2.26{col 57}{space 3}0.024{col 65}{space 4} -.019035{col 78}{space 3}-.0013578
{txt}{space 13}highschool {c |}{col 25}{res}{space 2} .0239526{col 37}{space 2} .0075669{col 48}{space 1}    3.17{col 57}{space 3}0.002{col 65}{space 4}  .009121{col 78}{space 3} .0387843
{txt}{space 16}college {c |}{col 25}{res}{space 2} .0670183{col 37}{space 2} .0066756{col 48}{space 1}   10.04{col 57}{space 3}0.000{col 65}{space 4} .0539336{col 78}{space 3}  .080103
{txt}{space 13}noreligion {c |}{col 25}{res}{space 2} .0394337{col 37}{space 2} .0116877{col 48}{space 1}    3.37{col 57}{space 3}0.001{col 65}{space 4} .0165249{col 78}{space 3} .0623424
{txt}{space 3}christiannotcatholic {c |}{col 25}{res}{space 2} .0293499{col 37}{space 2} .0172298{col 48}{space 1}    1.70{col 57}{space 3}0.088{col 65}{space 4}-.0044217{col 78}{space 3} .0631216
{txt}{space 15}catholic {c |}{col 25}{res}{space 2} .0354713{col 37}{space 2} .0109128{col 48}{space 1}    3.25{col 57}{space 3}0.001{col 65}{space 4} .0140814{col 78}{space 3} .0568612
{txt}{space 9}fulltimeworker {c |}{col 25}{res}{space 2} .0166043{col 37}{space 2}  .065966{col 48}{space 1}    0.25{col 57}{space 3}0.801{col 65}{space 4}-.1126938{col 78}{space 3} .1459023
{txt}{space 9}parttimeworker {c |}{col 25}{res}{space 2} .0424428{col 37}{space 2} .0677941{col 48}{space 1}    0.63{col 57}{space 3}0.531{col 65}{space 4}-.0904383{col 78}{space 3}  .175324
{txt}{space 13}unemployed {c |}{col 25}{res}{space 2}  .012916{col 37}{space 2} .0746332{col 48}{space 1}    0.17{col 57}{space 3}0.863{col 65}{space 4}-.1333702{col 78}{space 3} .1592022
{txt}{space 11}selfemployed {c |}{col 25}{res}{space 2} .0137822{col 37}{space 2} .0825596{col 48}{space 1}    0.17{col 57}{space 3}0.867{col 65}{space 4}-.1480404{col 78}{space 3} .1756047
{txt}{space 16}outofLF {c |}{col 25}{res}{space 2}  .029226{col 37}{space 2} .0687087{col 48}{space 1}    0.43{col 57}{space 3}0.671{col 65}{space 4}-.1054478{col 78}{space 3} .1638997
{txt}{space 13}goodhealth {c |}{col 25}{res}{space 2} .0274864{col 37}{space 2} .0113374{col 48}{space 1}    2.42{col 57}{space 3}0.015{col 65}{space 4} .0052643{col 78}{space 3} .0497084
{txt}{space 18}white {c |}{col 25}{res}{space 2} .0506319{col 37}{space 2} .0648562{col 48}{space 1}    0.78{col 57}{space 3}0.435{col 65}{space 4}-.0764907{col 78}{space 3} .1777545
{txt}{space 18}black {c |}{col 25}{res}{space 2}  .069515{col 37}{space 2} .0642737{col 48}{space 1}    1.08{col 57}{space 3}0.279{col 65}{space 4} -.056466{col 78}{space 3}  .195496
{txt}{space 17}latino {c |}{col 25}{res}{space 2} .0965484{col 37}{space 2} .0684786{col 48}{space 1}    1.41{col 57}{space 3}0.159{col 65}{space 4}-.0376744{col 78}{space 3} .2307712
{txt}{space 18}asian {c |}{col 25}{res}{space 2} .0816873{col 37}{space 2} .0690595{col 48}{space 1}    1.18{col 57}{space 3}0.237{col 65}{space 4}-.0536741{col 78}{space 3} .2170487
{txt}{space 12}whitecollar {c |}{col 25}{res}{space 2}-.0120083{col 37}{space 2} .0102888{col 48}{space 1}   -1.17{col 57}{space 3}0.243{col 65}{space 4}-.0321751{col 78}{space 3} .0081585
{txt}{space 13}bluecollar {c |}{col 25}{res}{space 2}-.0207131{col 37}{space 2} .0123368{col 48}{space 1}   -1.68{col 57}{space 3}0.093{col 65}{space 4}-.0448941{col 78}{space 3}  .003468
{txt}{space 10}serviceworker {c |}{col 25}{res}{space 2}-.0112827{col 37}{space 2} .0086907{col 48}{space 1}   -1.30{col 57}{space 3}0.194{col 65}{space 4}-.0283171{col 78}{space 3} .0057517
{txt}{space 12}mv_thirties {c |}{col 25}{res}{space 2}        0{col 37}{txt}  (omitted)
{space 12}mv_fourties {c |}{col 25}{res}{space 2}        0{col 37}{txt}  (omitted)
{space 13}mv_fifties {c |}{col 25}{res}{space 2}        0{col 37}{txt}  (omitted)
{space 13}mv_sixties {c |}{col 25}{res}{space 2}        0{col 37}{txt}  (omitted)
{space 11}mv_seventies {c |}{col 25}{res}{space 2}        0{col 37}{txt}  (omitted)
{space 5}mv_income2quartile {c |}{col 25}{res}{space 2}        0{col 37}{txt}  (omitted)
{space 5}mv_income3quartile {c |}{col 25}{res}{space 2}        0{col 37}{txt}  (omitted)
{space 5}mv_income4quartile {c |}{col 25}{res}{space 2}        0{col 37}{txt}  (omitted)
{space 6}mv_incomenoanswer {c |}{col 25}{res}{space 2} .0005538{col 37}{space 2} .0199732{col 48}{space 1}    0.03{col 57}{space 3}0.978{col 65}{space 4}-.0385949{col 78}{space 3} .0397026
{txt}{space 14}mv_female {c |}{col 25}{res}{space 2}        0{col 37}{txt}  (omitted)
{space 10}mv_highschool {c |}{col 25}{res}{space 2} .0251337{col 37}{space 2} .0175051{col 48}{space 1}    1.44{col 57}{space 3}0.151{col 65}{space 4}-.0091775{col 78}{space 3} .0594449
{txt}{space 13}mv_college {c |}{col 25}{res}{space 2}        0{col 37}{txt}  (omitted)
{space 10}mv_noreligion {c |}{col 25}{res}{space 2} .0317691{col 37}{space 2} .0266812{col 48}{space 1}    1.19{col 57}{space 3}0.234{col 65}{space 4} -.020528{col 78}{space 3} .0840662
{txt}mv_christiannotcatholic {c |}{col 25}{res}{space 2}        0{col 37}{txt}  (omitted)
{space 12}mv_catholic {c |}{col 25}{res}{space 2}        0{col 37}{txt}  (omitted)
{space 6}mv_fulltimeworker {c |}{col 25}{res}{space 2}        0{col 37}{txt}  (omitted)
{space 6}mv_parttimeworker {c |}{col 25}{res}{space 2}   .03905{col 37}{space 2} .0641943{col 48}{space 1}    0.61{col 57}{space 3}0.543{col 65}{space 4}-.0867752{col 78}{space 3} .1648753
{txt}{space 10}mv_unemployed {c |}{col 25}{res}{space 2}        0{col 37}{txt}  (omitted)
{space 8}mv_selfemployed {c |}{col 25}{res}{space 2} -.066599{col 37}{space 2} .0657347{col 48}{space 1}   -1.01{col 57}{space 3}0.311{col 65}{space 4}-.1954436{col 78}{space 3} .0622457
{txt}{space 13}mv_outofLF {c |}{col 25}{res}{space 2}        0{col 37}{txt}  (omitted)
{space 10}mv_goodhealth {c |}{col 25}{res}{space 2}-.1839394{col 37}{space 2}  .141094{col 48}{space 1}   -1.30{col 57}{space 3}0.192{col 65}{space 4}-.4604934{col 78}{space 3} .0926146
{txt}{space 15}mv_white {c |}{col 25}{res}{space 2} .1285453{col 37}{space 2} .0988659{col 48}{space 1}    1.30{col 57}{space 3}0.194{col 65}{space 4}-.0652387{col 78}{space 3} .3223293
{txt}{space 15}mv_black {c |}{col 25}{res}{space 2}        0{col 37}{txt}  (omitted)
{space 14}mv_latino {c |}{col 25}{res}{space 2}        0{col 37}{txt}  (omitted)
{space 15}mv_asian {c |}{col 25}{res}{space 2}        0{col 37}{txt}  (omitted)
{space 9}mv_whitecollar {c |}{col 25}{res}{space 2}-.0705549{col 37}{space 2} .0668946{col 48}{space 1}   -1.05{col 57}{space 3}0.292{col 65}{space 4}-.2016729{col 78}{space 3} .0605631
{txt}{space 10}mv_bluecollar {c |}{col 25}{res}{space 2}        0{col 37}{txt}  (omitted)
{space 7}mv_serviceworker {c |}{col 25}{res}{space 2}        0{col 37}{txt}  (omitted)
{space 23} {c |}
{space 16}country {c |}
{space 15}Austria  {c |}{col 25}{res}{space 2} .1204074{col 37}{space 2}  .124201{col 48}{space 1}    0.97{col 57}{space 3}0.332{col 65}{space 4}-.1230353{col 78}{space 3} .3638501
{txt}{space 16}Brazil  {c |}{col 25}{res}{space 2}-.0698866{col 37}{space 2} .0217084{col 48}{space 1}   -3.22{col 57}{space 3}0.001{col 65}{space 4}-.1124365{col 78}{space 3}-.0273366
{txt}{space 16}France  {c |}{col 25}{res}{space 2}        0{col 37}{txt}  (omitted)
{space 15}Germany  {c |}{col 25}{res}{space 2}  .062125{col 37}{space 2} .1312827{col 48}{space 1}    0.47{col 57}{space 3}0.636{col 65}{space 4}-.1951981{col 78}{space 3} .3194482
{txt}{space 17}Italy  {c |}{col 25}{res}{space 2} .1044462{col 37}{space 2} .1223026{col 48}{space 1}    0.85{col 57}{space 3}0.393{col 65}{space 4}-.1352754{col 78}{space 3} .3441678
{txt}{space 11}New_Zealand  {c |}{col 25}{res}{space 2}        0{col 37}{txt}  (omitted)
{space 16}Poland  {c |}{col 25}{res}{space 2} -.105539{col 37}{space 2} .0233474{col 48}{space 1}   -4.52{col 57}{space 3}0.000{col 65}{space 4}-.1513015{col 78}{space 3}-.0597766
{txt}{space 17}Spain  {c |}{col 25}{res}{space 2}        0{col 37}{txt}  (omitted)
{space 16}Sweden  {c |}{col 25}{res}{space 2} -.066631{col 37}{space 2} .0239689{col 48}{space 1}   -2.78{col 57}{space 3}0.005{col 65}{space 4}-.1136117{col 78}{space 3}-.0196502
{txt}{space 20}UK  {c |}{col 25}{res}{space 2}        0{col 37}{txt}  (omitted)
{space 20}US  {c |}{col 25}{res}{space 2}        0{col 37}{txt}  (omitted)
{space 23} {c |}
{space 18}_cons {c |}{col 25}{res}{space 2} .6636018{col 37}{space 2} .0633164{col 48}{space 1}   10.48{col 57}{space 3}0.000{col 65}{space 4} .5394972{col 78}{space 3} .7877065
{txt}{hline 24}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{help ivreg2##idtest:Underidentification test} (Kleibergen-Paap rk LM statistic):{res}{col 71}  23.663
{txt}{col 52}Chi-sq({res}15{txt}) P-val =  {res}{col 73}0.0710
{txt}{hline 78}
{help ivreg2##widtest:Weak identification test} (Cragg-Donald Wald F statistic):{res}{col 71}   1.551
{txt}                         (Kleibergen-Paap rk Wald F statistic):{res}{col 71}   1.580
{txt}Stock-Yogo weak ID test critical values:{res}{txt}{col 42} 5% maximal IV relative bias{res}{col 73} 21.23
{txt}{col 42}10% maximal IV relative bias{res}{col 73} 11.51
{txt}{col 42}20% maximal IV relative bias{res}{col 73}  6.42
{txt}{col 42}30% maximal IV relative bias{res}{col 73}  4.63
{txt}{col 42}10% maximal IV size{res}{col 73} 50.39
{txt}{col 42}15% maximal IV size{res}{col 73} 26.80
{txt}{col 42}20% maximal IV size{res}{col 73} 18.72
{txt}{col 42}25% maximal IV size{res}{col 73} 14.60
{txt}Source: Stock-Yogo (2005).  Reproduced by permission.
NB: Critical values are for Cragg-Donald F statistic and i.i.d. errors.
{hline 78}
{help ivreg2##overidtests:Hansen J statistic} (overidentification test of all instruments):{res}{col 71}  13.744
{txt}{col 52}Chi-sq({res}14{txt}) P-val =  {res}{col 73}0.4690
{txt}{hline 78}
Instrumented:{col 23}satis_head
Included instruments:{col 23}thirties fourties fifties sixties seventies
{col 23}income2quartile income3quartile income4quartile
{col 23}incomenoanswer female highschool college noreligion
{col 23}christiannotcatholic catholic fulltimeworker
{col 23}parttimeworker unemployed selfemployed outofLF goodhealth
{col 23}white black latino asian whitecollar bluecollar
{col 23}serviceworker mv_incomenoanswer mv_highschool
{col 23}mv_noreligion mv_parttimeworker mv_selfemployed
{col 23}mv_goodhealth mv_white mv_whitecollar 2.country 3.country
{col 23}5.country 6.country 8.country 10.country
Excluded instruments:{col 23}TH1_TE1 TH1_TE2 TH1_TE3 TH1_TE4 TH2_TE1 TH2_TE2 TH2_TE3
{col 23}TH2_TE4 TH3_TE1 TH3_TE2 TH3_TE3 TH3_TE4 TH4_TE1 TH4_TE2
{col 23}TH4_TE3
Dropped collinear:{col 23}mv_thirties mv_fourties mv_fifties mv_sixties
{col 23}mv_seventies mv_income2quartile mv_income3quartile
{col 23}mv_income4quartile mv_female mv_college
{col 23}mv_christiannotcatholic mv_catholic mv_fulltimeworker
{col 23}mv_unemployed mv_outofLF mv_black mv_latino mv_asian
{col 23}mv_bluecollar mv_serviceworker 4.country 7.country
{col 23}9.country 11.country 12.country
{hline 78}
({res}est7{txt} stored)

{com}. 
. estadd scalar fstat = e(widstat)

{txt}added scalar:
              e(fstat) =  {res}1.5801279
{txt}
{com}. estadd local IV "16 IVs", replace

{txt}added macro:
                 e(IV) : "{res:16 IVs}"

{com}. estadd local CFE "X", replace

{txt}added macro:
                e(CFE) : "{res:X}"

{com}. estadd local Controls "X", replace

{txt}added macro:
           e(Controls) : "{res:X}"

{com}. get_mean

{txt}added scalar:
                 e(av) =  {res}.902
{txt}
{com}. 
. /* 4. Col 8: Democracy with 1 instrument*/      
. * to get the Cragg-Donald statistic: 
. eststo: ivreg2 democ ///
>         (satis_head = treatc ) $controls $mv_controls  i.country, small robust 
{txt}Warning - collinearities detected
Vars dropped:{col 21}mv_thirties mv_fourties mv_fifties mv_sixties mv_seventies
{col 21}mv_income2quartile mv_income3quartile mv_income4quartile
{col 21}mv_female mv_college mv_christiannotcatholic mv_catholic
{col 21}mv_fulltimeworker mv_unemployed mv_outofLF mv_black
{col 21}mv_latino mv_asian mv_bluecollar mv_serviceworker 4.country
{col 21}7.country 9.country 11.country 12.country
{res}
{txt}IV (2SLS) estimation
{hline 20}

Estimates efficient for homoskedasticity only
Statistics robust to heteroskedasticity

{col 55}Number of obs = {res}   22537
{txt}{col 55}F( 43, 22493) = {res}   18.93
{txt}{col 55}Prob > F      = {res}  0.0000
{txt}Total (centered) SS     = {res} 1987.655944{txt}{col 55}Centered R2   = {res}  0.0434
{txt}Total (uncentered) SS   = {res}       20334{txt}{col 55}Uncentered R2 = {res}  0.9065
{txt}Residual SS             = {res} 1901.472941{txt}{col 55}Root MSE      = {res}   .2908

{txt}{hline 24}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 25}{c |}{col 37}    Robust
{col 1}                  democ{col 25}{c |} Coefficient{col 37}  std. err.{col 49}      t{col 57}   P>|t|{col 65}     [95% con{col 78}f. interval]
{hline 24}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 13}satis_head {c |}{col 25}{res}{space 2} .0557127{col 37}{space 2} .3113813{col 48}{space 1}    0.18{col 57}{space 3}0.858{col 65}{space 4}-.5546164{col 78}{space 3} .6660417
{txt}{space 15}thirties {c |}{col 25}{res}{space 2} .0054657{col 37}{space 2} .0089344{col 48}{space 1}    0.61{col 57}{space 3}0.541{col 65}{space 4}-.0120464{col 78}{space 3} .0229778
{txt}{space 15}fourties {c |}{col 25}{res}{space 2} .0148267{col 37}{space 2} .0124684{col 48}{space 1}    1.19{col 57}{space 3}0.234{col 65}{space 4}-.0096123{col 78}{space 3} .0392656
{txt}{space 16}fifties {c |}{col 25}{res}{space 2}  .044685{col 37}{space 2} .0129742{col 48}{space 1}    3.44{col 57}{space 3}0.001{col 65}{space 4} .0192547{col 78}{space 3} .0701154
{txt}{space 16}sixties {c |}{col 25}{res}{space 2}  .067287{col 37}{space 2} .0133438{col 48}{space 1}    5.04{col 57}{space 3}0.000{col 65}{space 4} .0411322{col 78}{space 3} .0934418
{txt}{space 14}seventies {c |}{col 25}{res}{space 2} .0834387{col 37}{space 2}  .009081{col 48}{space 1}    9.19{col 57}{space 3}0.000{col 65}{space 4} .0656393{col 78}{space 3} .1012381
{txt}{space 8}income2quartile {c |}{col 25}{res}{space 2} .0290017{col 37}{space 2} .0081324{col 48}{space 1}    3.57{col 57}{space 3}0.000{col 65}{space 4} .0130617{col 78}{space 3} .0449418
{txt}{space 8}income3quartile {c |}{col 25}{res}{space 2} .0480037{col 37}{space 2} .0092566{col 48}{space 1}    5.19{col 57}{space 3}0.000{col 65}{space 4}   .02986{col 78}{space 3} .0661473
{txt}{space 8}income4quartile {c |}{col 25}{res}{space 2} .0529246{col 37}{space 2} .0139613{col 48}{space 1}    3.79{col 57}{space 3}0.000{col 65}{space 4} .0255595{col 78}{space 3} .0802898
{txt}{space 9}incomenoanswer {c |}{col 25}{res}{space 2} .0080347{col 37}{space 2} .0101733{col 48}{space 1}    0.79{col 57}{space 3}0.430{col 65}{space 4}-.0119056{col 78}{space 3}  .027975
{txt}{space 17}female {c |}{col 25}{res}{space 2}-.0096967{col 37}{space 2} .0051744{col 48}{space 1}   -1.87{col 57}{space 3}0.061{col 65}{space 4}-.0198389{col 78}{space 3} .0004454
{txt}{space 13}highschool {c |}{col 25}{res}{space 2} .0243793{col 37}{space 2} .0078225{col 48}{space 1}    3.12{col 57}{space 3}0.002{col 65}{space 4} .0090466{col 78}{space 3}  .039712
{txt}{space 16}college {c |}{col 25}{res}{space 2} .0671336{col 37}{space 2}  .006683{col 48}{space 1}   10.05{col 57}{space 3}0.000{col 65}{space 4} .0540344{col 78}{space 3} .0802328
{txt}{space 13}noreligion {c |}{col 25}{res}{space 2} .0379867{col 37}{space 2} .0137077{col 48}{space 1}    2.77{col 57}{space 3}0.006{col 65}{space 4} .0111187{col 78}{space 3} .0648546
{txt}{space 3}christiannotcatholic {c |}{col 25}{res}{space 2}  .032341{col 37}{space 2} .0227456{col 48}{space 1}    1.42{col 57}{space 3}0.155{col 65}{space 4} -.012242{col 78}{space 3}  .076924
{txt}{space 15}catholic {c |}{col 25}{res}{space 2} .0364823{col 37}{space 2} .0119954{col 48}{space 1}    3.04{col 57}{space 3}0.002{col 65}{space 4} .0129705{col 78}{space 3} .0599941
{txt}{space 9}fulltimeworker {c |}{col 25}{res}{space 2} .0020198{col 37}{space 2} .0979692{col 48}{space 1}    0.02{col 57}{space 3}0.984{col 65}{space 4}-.1900067{col 78}{space 3} .1940463
{txt}{space 9}parttimeworker {c |}{col 25}{res}{space 2} .0281566{col 37}{space 2} .0980688{col 48}{space 1}    0.29{col 57}{space 3}0.774{col 65}{space 4}-.1640651{col 78}{space 3} .2203783
{txt}{space 13}unemployed {c |}{col 25}{res}{space 2}-.0032424{col 37}{space 2} .1097963{col 48}{space 1}   -0.03{col 57}{space 3}0.976{col 65}{space 4}-.2184508{col 78}{space 3}  .211966
{txt}{space 11}selfemployed {c |}{col 25}{res}{space 2}-.0034615{col 37}{space 2} .1193445{col 48}{space 1}   -0.03{col 57}{space 3}0.977{col 65}{space 4} -.237385{col 78}{space 3}  .230462
{txt}{space 16}outofLF {c |}{col 25}{res}{space 2} .0140752{col 37}{space 2} .1020713{col 48}{space 1}    0.14{col 57}{space 3}0.890{col 65}{space 4}-.1859916{col 78}{space 3}  .214142
{txt}{space 13}goodhealth {c |}{col 25}{res}{space 2} .0298175{col 37}{space 2}   .01613{col 48}{space 1}    1.85{col 57}{space 3}0.065{col 65}{space 4}-.0017984{col 78}{space 3} .0614334
{txt}{space 18}white {c |}{col 25}{res}{space 2} .0567811{col 37}{space 2} .0709273{col 48}{space 1}    0.80{col 57}{space 3}0.423{col 65}{space 4}-.0822414{col 78}{space 3} .1958036
{txt}{space 18}black {c |}{col 25}{res}{space 2}  .070021{col 37}{space 2} .0636612{col 48}{space 1}    1.10{col 57}{space 3}0.271{col 65}{space 4}-.0547593{col 78}{space 3} .1948014
{txt}{space 17}latino {c |}{col 25}{res}{space 2} .0981931{col 37}{space 2} .0684804{col 48}{space 1}    1.43{col 57}{space 3}0.152{col 65}{space 4}-.0360332{col 78}{space 3} .2324194
{txt}{space 18}asian {c |}{col 25}{res}{space 2} .0850786{col 37}{space 2} .0702341{col 48}{space 1}    1.21{col 57}{space 3}0.226{col 65}{space 4} -.052585{col 78}{space 3} .2227423
{txt}{space 12}whitecollar {c |}{col 25}{res}{space 2} -.012815{col 37}{space 2} .0108737{col 48}{space 1}   -1.18{col 57}{space 3}0.239{col 65}{space 4}-.0341282{col 78}{space 3} .0084982
{txt}{space 13}bluecollar {c |}{col 25}{res}{space 2}-.0218124{col 37}{space 2}  .013467{col 48}{space 1}   -1.62{col 57}{space 3}0.105{col 65}{space 4}-.0482086{col 78}{space 3} .0045839
{txt}{space 10}serviceworker {c |}{col 25}{res}{space 2}-.0117462{col 37}{space 2} .0089838{col 48}{space 1}   -1.31{col 57}{space 3}0.191{col 65}{space 4}-.0293551{col 78}{space 3} .0058627
{txt}{space 12}mv_thirties {c |}{col 25}{res}{space 2}        0{col 37}{txt}  (omitted)
{space 12}mv_fourties {c |}{col 25}{res}{space 2}        0{col 37}{txt}  (omitted)
{space 13}mv_fifties {c |}{col 25}{res}{space 2}        0{col 37}{txt}  (omitted)
{space 13}mv_sixties {c |}{col 25}{res}{space 2}        0{col 37}{txt}  (omitted)
{space 11}mv_seventies {c |}{col 25}{res}{space 2}        0{col 37}{txt}  (omitted)
{space 5}mv_income2quartile {c |}{col 25}{res}{space 2}        0{col 37}{txt}  (omitted)
{space 5}mv_income3quartile {c |}{col 25}{res}{space 2}        0{col 37}{txt}  (omitted)
{space 5}mv_income4quartile {c |}{col 25}{res}{space 2}        0{col 37}{txt}  (omitted)
{space 6}mv_incomenoanswer {c |}{col 25}{res}{space 2}-.0029114{col 37}{space 2} .0266005{col 48}{space 1}   -0.11{col 57}{space 3}0.913{col 65}{space 4}-.0550503{col 78}{space 3} .0492275
{txt}{space 14}mv_female {c |}{col 25}{res}{space 2}        0{col 37}{txt}  (omitted)
{space 10}mv_highschool {c |}{col 25}{res}{space 2} .0246075{col 37}{space 2} .0177894{col 48}{space 1}    1.38{col 57}{space 3}0.167{col 65}{space 4}-.0102611{col 78}{space 3}  .059476
{txt}{space 13}mv_college {c |}{col 25}{res}{space 2}        0{col 37}{txt}  (omitted)
{space 10}mv_noreligion {c |}{col 25}{res}{space 2} .0319935{col 37}{space 2} .0266809{col 48}{space 1}    1.20{col 57}{space 3}0.230{col 65}{space 4} -.020303{col 78}{space 3} .0842899
{txt}mv_christiannotcatholic {c |}{col 25}{res}{space 2}        0{col 37}{txt}  (omitted)
{space 12}mv_catholic {c |}{col 25}{res}{space 2}        0{col 37}{txt}  (omitted)
{space 6}mv_fulltimeworker {c |}{col 25}{res}{space 2}        0{col 37}{txt}  (omitted)
{space 6}mv_parttimeworker {c |}{col 25}{res}{space 2} .0255591{col 37}{space 2} .0930193{col 48}{space 1}    0.27{col 57}{space 3}0.783{col 65}{space 4}-.1567653{col 78}{space 3} .2078834
{txt}{space 10}mv_unemployed {c |}{col 25}{res}{space 2}        0{col 37}{txt}  (omitted)
{space 8}mv_selfemployed {c |}{col 25}{res}{space 2}-.0527482{col 37}{space 2} .0952431{col 48}{space 1}   -0.55{col 57}{space 3}0.580{col 65}{space 4}-.2394314{col 78}{space 3} .1339349
{txt}{space 13}mv_outofLF {c |}{col 25}{res}{space 2}        0{col 37}{txt}  (omitted)
{space 10}mv_goodhealth {c |}{col 25}{res}{space 2}-.1756552{col 37}{space 2}  .145173{col 48}{space 1}   -1.21{col 57}{space 3}0.226{col 65}{space 4}-.4602043{col 78}{space 3} .1088939
{txt}{space 15}mv_white {c |}{col 25}{res}{space 2} .1463233{col 37}{space 2} .1317746{col 48}{space 1}    1.11{col 57}{space 3}0.267{col 65}{space 4}-.1119642{col 78}{space 3} .4046107
{txt}{space 15}mv_black {c |}{col 25}{res}{space 2}        0{col 37}{txt}  (omitted)
{space 14}mv_latino {c |}{col 25}{res}{space 2}        0{col 37}{txt}  (omitted)
{space 15}mv_asian {c |}{col 25}{res}{space 2}        0{col 37}{txt}  (omitted)
{space 9}mv_whitecollar {c |}{col 25}{res}{space 2}-.0563952{col 37}{space 2} .0971216{col 48}{space 1}   -0.58{col 57}{space 3}0.561{col 65}{space 4}-.2467604{col 78}{space 3} .1339699
{txt}{space 10}mv_bluecollar {c |}{col 25}{res}{space 2}        0{col 37}{txt}  (omitted)
{space 7}mv_serviceworker {c |}{col 25}{res}{space 2}        0{col 37}{txt}  (omitted)
{space 23} {c |}
{space 16}country {c |}
{space 15}Austria  {c |}{col 25}{res}{space 2} .1444536{col 37}{space 2} .1715948{col 48}{space 1}    0.84{col 57}{space 3}0.400{col 65}{space 4}-.1918841{col 78}{space 3} .4807914
{txt}{space 16}Brazil  {c |}{col 25}{res}{space 2}-.0706637{col 37}{space 2} .0220902{col 48}{space 1}   -3.20{col 57}{space 3}0.001{col 65}{space 4}-.1139621{col 78}{space 3}-.0273653
{txt}{space 16}France  {c |}{col 25}{res}{space 2}        0{col 37}{txt}  (omitted)
{space 15}Germany  {c |}{col 25}{res}{space 2} .0879848{col 37}{space 2} .1828971{col 48}{space 1}    0.48{col 57}{space 3}0.630{col 65}{space 4}-.2705063{col 78}{space 3} .4464758
{txt}{space 17}Italy  {c |}{col 25}{res}{space 2} .1273019{col 37}{space 2} .1657204{col 48}{space 1}    0.77{col 57}{space 3}0.442{col 65}{space 4}-.1975217{col 78}{space 3} .4521255
{txt}{space 11}New_Zealand  {c |}{col 25}{res}{space 2}        0{col 37}{txt}  (omitted)
{space 16}Poland  {c |}{col 25}{res}{space 2}-.1071618{col 37}{space 2}  .024755{col 48}{space 1}   -4.33{col 57}{space 3}0.000{col 65}{space 4}-.1556833{col 78}{space 3}-.0586404
{txt}{space 17}Spain  {c |}{col 25}{res}{space 2}        0{col 37}{txt}  (omitted)
{space 16}Sweden  {c |}{col 25}{res}{space 2}-.0639059{col 37}{space 2} .0275226{col 48}{space 1}   -2.32{col 57}{space 3}0.020{col 65}{space 4}-.1178522{col 78}{space 3}-.0099597
{txt}{space 20}UK  {c |}{col 25}{res}{space 2}        0{col 37}{txt}  (omitted)
{space 20}US  {c |}{col 25}{res}{space 2}        0{col 37}{txt}  (omitted)
{space 23} {c |}
{space 18}_cons {c |}{col 25}{res}{space 2} .6632423{col 37}{space 2} .0628062{col 48}{space 1}   10.56{col 57}{space 3}0.000{col 65}{space 4} .5401379{col 78}{space 3} .7863467
{txt}{hline 24}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{help ivreg2##idtest:Underidentification test} (Kleibergen-Paap rk LM statistic):{res}{col 71}  10.115
{txt}{col 52}Chi-sq({res}1{txt}) P-val =  {res}{col 73}0.0015
{txt}{hline 78}
{help ivreg2##widtest:Weak identification test} (Cragg-Donald Wald F statistic):{res}{col 71}  10.084
{txt}                         (Kleibergen-Paap rk Wald F statistic):{res}{col 71}  10.111
{txt}Stock-Yogo weak ID test critical values:{res}{txt}{col 42}10% maximal IV size{res}{col 73} 16.38
{txt}{col 42}15% maximal IV size{res}{col 73}  8.96
{txt}{col 42}20% maximal IV size{res}{col 73}  6.66
{txt}{col 42}25% maximal IV size{res}{col 73}  5.53
{txt}Source: Stock-Yogo (2005).  Reproduced by permission.
NB: Critical values are for Cragg-Donald F statistic and i.i.d. errors.
{hline 78}
{help ivreg2##overidtests:Hansen J statistic} (overidentification test of all instruments):{res}{col 71}   0.000
{txt}{col 50}(equation exactly identified)
{hline 78}
Instrumented:{col 23}satis_head
Included instruments:{col 23}thirties fourties fifties sixties seventies
{col 23}income2quartile income3quartile income4quartile
{col 23}incomenoanswer female highschool college noreligion
{col 23}christiannotcatholic catholic fulltimeworker
{col 23}parttimeworker unemployed selfemployed outofLF goodhealth
{col 23}white black latino asian whitecollar bluecollar
{col 23}serviceworker mv_incomenoanswer mv_highschool
{col 23}mv_noreligion mv_parttimeworker mv_selfemployed
{col 23}mv_goodhealth mv_white mv_whitecollar 2.country 3.country
{col 23}5.country 6.country 8.country 10.country
Excluded instruments:{col 23}treatc
Dropped collinear:{col 23}mv_thirties mv_fourties mv_fifties mv_sixties
{col 23}mv_seventies mv_income2quartile mv_income3quartile
{col 23}mv_income4quartile mv_female mv_college
{col 23}mv_christiannotcatholic mv_catholic mv_fulltimeworker
{col 23}mv_unemployed mv_outofLF mv_black mv_latino mv_asian
{col 23}mv_bluecollar mv_serviceworker 4.country 7.country
{col 23}9.country 11.country 12.country
{hline 78}
({res}est8{txt} stored)

{com}. 
. estadd scalar fstat = e(widstat)

{txt}added scalar:
              e(fstat) =  {res}10.110687
{txt}
{com}. estadd local IV "SumIV", replace

{txt}added macro:
                 e(IV) : "{res:SumIV}"

{com}. estadd local CFE "X", replace

{txt}added macro:
                e(CFE) : "{res:X}"

{com}. estadd local Controls "X", replace

{txt}added macro:
           e(Controls) : "{res:X}"

{com}. get_mean

{txt}added scalar:
                 e(av) =  {res}.902
{txt}
{com}. 
. esttab using "3_output/1_main_paper/Tables/Table4.tex", depvar keep(satis_head) ///
>         label replace wrap ///
>         cells(b(star fmt(%15.3fc)) se(par fmt(%15.3fc))) interaction(" $/times$ ") ///
>         star(* 0.10 ** 0.05 *** 0.01) ///
>         stats(Controls CFE N av IV fstat , layout(@ @ @ @ @ @) fmt(%15s %15s %15.0fc %12.3f %9.3f %9.3f ) ///
>         labels("Individual controls" "Country FE" Observations "Outcome mean" "Instruments" "F-statistic" )) ////
>         collabels(none) mlabels(none) ///
>         mgroups("Strong leader" "Experts" "Army" "Democracy" , pattern(1 0 1 0 1 0 1 0 ) ///
>         prefix(\multicolumn{c -(}@span{c )-}{c -(}c{c )-}{c -(}) suffix({c )-}) span erepeat(\cmidrule(lr){c -(}@span{c )-}))
{res}{txt}(output written to {browse  `"3_output/1_main_paper/Tables/Table4.tex"'})

{com}. 
{txt}end of do-file

{com}. do "2_code/figures_main_paper.do"
{txt}
{com}. /*** *** *** *** *** *** *** *** *** *** *** *** *** *** *** *** *** ***
> Author: Nicolas Longuet Marx
> Date: January 23 2023
> Last modified: Aug 19 2023
> Object: Produce figures of the main paper
> Databases in input: A_experiment.dta
> Databases in output: None
> 
> Figures generated in this script:
> 
> - Figure 1: Effect of randomized messages on satisfaction with policy response
>         . Figure 1a: Health treatment on health evaluation
>         . Figure 1b: Economic treatment on economic evaluation
> - Figure 2: Effect of randomized messages on satisfaction with head of government
> 
> *** *** *** *** *** *** *** *** *** *** *** *** *** *** *** *** *** ****/
. 
. set scheme s2mono
{txt}
{com}. grstyle init
{res}{txt}
{com}. grstyle set plain, horizontal grid dotted
{txt}
{com}. 
. use "1_data/A_experiment.dta", replace
{txt}
{com}. 
. gen H_treat = 1 if TH2 ==1
{txt}(16,856 missing values generated)

{com}. replace H_treat = 2 if TH1 ==1 
{txt}(5,572 real changes made)

{com}. replace H_treat = 3 if TH3 ==1 
{txt}(5,638 real changes made)

{com}. replace H_treat = 4 if TH4 ==1 
{txt}(5,646 real changes made)

{com}. lab var H_treat "Health treatment"
{txt}
{com}. 
. label define H_treat_lab 1 "Health good & praise gov't" 2 "Health good" 3 "Health bad" 4 "Health bad & blame gov't"
{txt}
{com}. lab values H_treat H_treat_lab
{txt}
{com}. 
. gen E_treat = 1 if TE2 ==1
{txt}(16,930 missing values generated)

{com}. replace E_treat = 2 if TE1 ==1 
{txt}(5,623 real changes made)

{com}. replace E_treat = 3 if TE3 ==1 
{txt}(5,663 real changes made)

{com}. replace E_treat = 4 if TE4 ==1 
{txt}(5,644 real changes made)

{com}. lab var H_treat "Economy treatment"
{txt}
{com}. 
. label define E_treat_lab 1 "Econ good & praise gov't " 2 "Econ good" 3 "Econ bad" 4 "Econ bad & blame gov't"
{txt}
{com}. lab values E_treat E_treat_lab
{txt}
{com}. 
. **# Figure 1: Effect of randomized messages on satisfaction with policy response  
. 
. **# Figure 1a: Health treatment on health evaluation 
. forval k = 1/3 {c -(}
{txt}  2{com}.         qui reg eval_sant TH2 TH1 TH3, robust
{txt}  3{com}. 
.         qui lincom TH`k'
{txt}  4{com}.         if r(p)<=0.1 {c -(}                                                  
{txt}  5{com}.                 if r(p)<=0.05 {c -(}
{txt}  6{com}.                         if r(p)<=0.01 {c -(}
{txt}  7{com}.                                  local signilab`k' = "***"
{txt}  8{com}.                         {c )-}
{txt}  9{com}.                         else {c -(}
{txt} 10{com}.                                  local signilab`k' = "**"
{txt} 11{com}.                         {c )-}
{txt} 12{com}.                 {c )-}
{txt} 13{com}.                 else {c -(}
{txt} 14{com}.                          local signilab`k' = "*"
{txt} 15{com}.                 {c )-}
{txt} 16{com}.         {c )-}
{txt} 17{com}.         qui lincom TH`k' + _cons
{txt} 18{com}.         local pos`k' = r(ub)
{txt} 19{com}.         di "`pos`k'" 
{txt} 20{com}.         di "signilab`k'" 
{txt} 21{com}. {c )-}
`pos1
signilab1
`pos2
signilab2
`pos3
signilab3
{txt}
{com}. reg eval_sant TH4

{txt}      Source {c |}       SS           df       MS      Number of obs   ={res}    22,541
{txt}{hline 13}{c +}{hline 34}   F(1, 22539)     = {res}    34.80
{txt}       Model {c |} {res} 2.52868024         1  2.52868024   {txt}Prob > F        ={res}    0.0000
{txt}    Residual {c |} {res} 1637.68739    22,539  .072660162   {txt}R-squared       ={res}    0.0015
{txt}{hline 13}{c +}{hline 34}   Adj R-squared   ={res}    0.0015
{txt}       Total {c |} {res} 1640.21607    22,540  .072769125   {txt}Root MSE        =   {res} .26956

{txt}{hline 13}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 1}   eval_sant{col 14}{c |} Coefficient{col 26}  Std. err.{col 38}      t{col 46}   P>|t|{col 54}     [95% con{col 67}f. interval]
{hline 13}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 9}TH4 {c |}{col 14}{res}{space 2}-.0244447{col 26}{space 2} .0041437{col 37}{space 1}   -5.90{col 46}{space 3}0.000{col 54}{space 4}-.0325665{col 67}{space 3}-.0163228
{txt}{space 7}_cons {c |}{col 14}{res}{space 2} .5149689{col 26}{space 2} .0020738{col 37}{space 1}  248.32{col 46}{space 3}0.000{col 54}{space 4} .5109041{col 67}{space 3} .5190337
{txt}{hline 13}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{res}{txt}
{com}. qui lincom TH4 + _cons +0.002
{txt}
{com}. local pos4  = r(ub)
{txt}
{com}. cibar eval_sant, over1(H_treat) barc(dknavy navy midblue eltblue) ciopts(lcolor(black)) graphopts(text(`pos2' 1.1 "`signilab2'" `pos1' 2.1 "`signilab1'"  `pos3' 3.1 "`signilab3'"  ,size(large)) text(`pos4' 4.1 "(Ref)", size(small)) ytitle("Satisfaction with health measures") xlabel(1 "Health good & praise gov't" 2 "Health good" 3 "Health bad" 4 "Health bad & blame gov't", labsize(small) nogrid)  legend(off) yla(,nogrid)  yscale(range(0.48 0.53)))
{res}{txt}
{com}. graph export "3_output/1_main_paper/Figures/Figure1a.pdf", as(pdf) replace
{txt}{p 0 4 2}
file {bf}
3_output/1_main_paper/Figures/Figure1a.pdf{rm}
saved as
PDF
format
{p_end}

{com}. 
. 
. **# Figure 1b: Economic treatment on economic evaluation 
. forval k = 1/3 {c -(}
{txt}  2{com}.         qui reg eval_eco TE2 TE1 TE3, robust
{txt}  3{com}. 
.         qui lincom TE`k'
{txt}  4{com}.         if r(p)<=0.1 {c -(}                                                  
{txt}  5{com}.                 if r(p)<=0.05 {c -(}
{txt}  6{com}.                         if r(p)<=0.01 {c -(}
{txt}  7{com}.                                  local signilab`k' = "***"
{txt}  8{com}.                         {c )-}
{txt}  9{com}.                         else {c -(}
{txt} 10{com}.                                  local signilab`k' = "**"
{txt} 11{com}.                         {c )-}
{txt} 12{com}.                 {c )-}
{txt} 13{com}.                 else {c -(}
{txt} 14{com}.                          local signilab`k' = "*"
{txt} 15{com}.                 {c )-}
{txt} 16{com}.         {c )-}
{txt} 17{com}.         qui lincom TE`k' + _cons
{txt} 18{com}.         local pos`k' = r(ub)
{txt} 19{com}.         di "`pos`k''" 
{txt} 20{com}.         di "`signilab`k''" 
{txt} 21{com}. {c )-}
.5137721962170126
***
.5179654305128359
***
.5059611055596525
**
{txt}
{com}. reg eval_eco TE4

{txt}      Source {c |}       SS           df       MS      Number of obs   ={res}    22,541
{txt}{hline 13}{c +}{hline 34}   F(1, 22539)     = {res}    17.26
{txt}       Model {c |} {res} 1.15204385         1  1.15204385   {txt}Prob > F        ={res}    0.0000
{txt}    Residual {c |} {res} 1504.30522    22,539  .066742323   {txt}R-squared       ={res}    0.0008
{txt}{hline 13}{c +}{hline 34}   Adj R-squared   ={res}    0.0007
{txt}       Total {c |} {res} 1505.45727    22,540  .066790473   {txt}Root MSE        =   {res} .25835

{txt}{hline 13}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 1}    eval_eco{col 14}{c |} Coefficient{col 26}  Std. err.{col 38}      t{col 46}   P>|t|{col 54}     [95% con{col 67}f. interval]
{hline 13}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 9}TE4 {c |}{col 14}{res}{space 2}-.0165015{col 26}{space 2} .0039718{col 37}{space 1}   -4.15{col 46}{space 3}0.000{col 54}{space 4}-.0242865{col 67}{space 3}-.0087164
{txt}{space 7}_cons {c |}{col 14}{res}{space 2} .5057998{col 26}{space 2} .0019874{col 37}{space 1}  254.50{col 46}{space 3}0.000{col 54}{space 4} .5019043{col 67}{space 3} .5096954
{txt}{hline 13}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{res}{txt}
{com}. qui lincom TE4 + _cons +0.002
{txt}
{com}. local pos4  = r(ub)
{txt}
{com}. 
. cibar eval_eco, over1(E_treat) barc(maroon cranberry red orange) ciopts(lcolor(black)) graphopts(text(`pos2' 1.1 "`signilab2'" `pos1' 2.1 "`signilab1'"  `pos3' 3.1 "`signilab3'"  ,size(large)) text(`pos4' 4.1 "(Ref)", size(small)) ytitle("Satisfaction with health measures") xlabel(1 "Econ good & praise gov't" 2 "Econ good" 3 "Econ bad" 4 "Econ bad & blame gov't", labsize(small) nogrid)  legend(off) yla(.48(0.01).53,nogrid) yscale(range(0.48 0.53))) 
{res}{txt}
{com}. graph export "3_output/1_main_paper/Figures/Figure1b.pdf", as(pdf) replace
{txt}{p 0 4 2}
file {bf}
3_output/1_main_paper/Figures/Figure1b.pdf{rm}
saved as
PDF
format
{p_end}

{com}. 
. 
. 
. **# Figure 2: Effect of randomized messages on satisfaction with head of government 
. forval k = 1/4 {c -(}
{txt}  2{com}.         forval j = 1/4 {c -(}
{txt}  3{com}.                 qui reg satis_head TH1_TE1 TH1_TE2 TH1_TE3 TH1_TE4 TH2_TE1 TH2_TE2 TH2_TE3 TH2_TE4 TH3_TE1 TH3_TE2 TH3_TE3 TH3_TE4 TH4_TE1 TH4_TE2 TH4_TE3, robust
{txt}  4{com}.         if `k'!=4 | `j'!=4 {c -(}
{txt}  5{com}.                 
.         
.                         qui lincom TH`k'_TE`j'
{txt}  6{com}.                         if r(p)<=0.1 {c -(}                                                  
{txt}  7{com}.                                 if r(p)<=0.05 {c -(}
{txt}  8{com}.                                         if r(p)<=0.01 {c -(}
{txt}  9{com}.                                                  local signilab`k'_`j' = "***"
{txt} 10{com}.                                         {c )-}
{txt} 11{com}.                                         else {c -(}
{txt} 12{com}.                                                  local signilab`k'_`j' = "**"
{txt} 13{com}.                                         {c )-}
{txt} 14{com}.                                 {c )-}
{txt} 15{com}.                                 else {c -(}
{txt} 16{com}.                                          local signilab`k'_`j' = "*"
{txt} 17{com}.                                 {c )-}
{txt} 18{com}.                         {c )-}
{txt} 19{com}.                         qui lincom TH`k'_TE`j' + _cons
{txt} 20{com}.                         local pos`k'_`j' = r(ub)+0.001
{txt} 21{com}.                 {c )-}
{txt} 22{com}.         {c )-}
{txt} 23{com}. {c )-}
{txt}
{com}. reg satis_head TH4_TE4

{txt}      Source {c |}       SS           df       MS      Number of obs   ={res}    22,541
{txt}{hline 13}{c +}{hline 34}   F(1, 22539)     = {res}     3.96
{txt}       Model {c |} {res} .390184669         1  .390184669   {txt}Prob > F        ={res}    0.0467
{txt}    Residual {c |} {res} 2222.92805    22,539  .098625851   {txt}R-squared       ={res}    0.0002
{txt}{hline 13}{c +}{hline 34}   Adj R-squared   ={res}    0.0001
{txt}       Total {c |} {res} 2223.31824    22,540  .098638786   {txt}Root MSE        =   {res} .31405

{txt}{hline 13}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 1}  satis_head{col 14}{c |} Coefficient{col 26}  Std. err.{col 38}      t{col 46}   P>|t|{col 54}     [95% con{col 67}f. interval]
{hline 13}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 5}TH4_TE4 {c |}{col 14}{res}{space 2}-.0171528{col 26}{space 2} .0086237{col 37}{space 1}   -1.99{col 46}{space 3}0.047{col 54}{space 4}-.0340559{col 67}{space 3}-.0002497
{txt}{space 7}_cons {c |}{col 14}{res}{space 2} .4592729{col 26}{space 2} .0021607{col 37}{space 1}  212.56{col 46}{space 3}0.000{col 54}{space 4} .4550379{col 67}{space 3}  .463508
{txt}{hline 13}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{res}{txt}
{com}. qui lincom TH4_TE4 + _cons +0.002
{txt}
{com}. local pos4_4  = r(ub)
{txt}
{com}. 
. 
. cibar satis_head, over1(H_treat) over2(E_treat)  barc(dknavy navy midblue eltblue) ciopts(lcolor(black)) ///
> graphopts(text(`pos2_2' 1.1 "`signilab2_2'" `pos1_2' 2.1 "`signilab1_2'" `pos3_2' 3.1 "`signilab3_2'" `pos4_2' 4.1 "`signilab4_2'" ///
> `pos2_1' 5.6 "`signilab2_1'" `pos1_1' 6.6 "`signilab1_1'" `pos3_1' 7.6 "`signilab3_1'" `pos4_1' 8.6 "`signilab4_1'" ///
> `pos2_3' 10.2 "`signilab2_3'" `pos1_3' 11.2 "`signilab1_3'" `pos3_3' 13.2 "`signilab3_3'" `pos4_3' 14.2 "`signilab4_3'" ///
> `pos2_4' 14.9 "`signilab2_4'" `pos1_4' 15.9 "`signilab1_4'" `pos3_4' 16.9 "`signilab3_4'" `pos4_4' 18 "(Ref)", size(small) ) ytitle("Satisfaction with the head of gov't")  yla(,nogrid) xla(,nogrid labsize(vsmall))  legend(cols(1) ) )
{res}{txt}
{com}. graph export "3_output/1_main_paper/Figures/Figure2.pdf", as(pdf) replace
{txt}{p 0 4 2}
file {bf}
3_output/1_main_paper/Figures/Figure2.pdf{rm}
saved as
PDF
format
{p_end}

{com}. 
{txt}end of do-file

{com}. do "2_code/tables_OA.do"
{txt}
{com}. /*** *** *** *** *** *** *** *** *** *** *** *** *** *** *** *** *** ***
> Author:Nicolas Longuet Marx
> Date: June 20 2020
> Last modified: August 19 2023
> Object: Produce Tables for the Online Appendix (OA)
> Databases in input: A_experiment.dta
> Databases in output: None
> 
> Tables generated in this script:
> 
> - Table E.2. Descriptive statistics and balance tests.
> - Table E.3. Impact on the perceived seriousness of the health and economic consequences of the crisis.
> - Table E.4. Impact on satisfaction with the government's response to the crisis.
> - Table E.5. Satisfaction with the health and economic measures, first stage.
> - Table E.6. Satisfaction with the head of government, first stage.
> - Table E.7. OLS estimates of the correlation between satisfaction with the head of government and attitudes on democracy.
> - Table E.8. Impact on political efficacy - 2SLS.
> - Table E.9: Impact on satisfaction with democracy, excluding Brazil and Poland - 2SLS.
> - Table E.10: Impact on support for democratic ideals, excluding Brazil and Poland - 2SLS.
> - Table E.12: Impact on satisfaction with democracy, depending on exposure to Covid-19 
> - Table E.14: Impact on satisfaction with democracy, treatments mentioning government - 2SLS.
> - Table E.15: Impact on support for democracy, treatments mentioning government - 2SLS. 
> - Table E.16: Impact on satisfaction with democracy, additional regressors - 2SLS.
> - Table E.17: Impact on support for democracy, additional regressors - 2SLS.
> - Table E.18: Impact of the health and economic consequences of the crisis on satisfaction with the regional government.
> - Table E.19: Impact of satisfaction with the regional government on satisfaction with democracy.
> *** *** *** *** *** *** *** *** *** *** *** *** *** *** *** *** *** ****/
. 
. **# 0. Prepare data 
. 
. use "1_data/A_experiment.dta", replace
{txt}
{com}. 
. 
. /* program to export estimation results better */
. qui do "2_code/mylincom_program.do"
{txt}
{com}. qui do "2_code/get_mean_program.do"
{txt}
{com}. qui do "2_code/mylincom_program_2sls.do"
{txt}
{com}. 
. 
. /* CONTROLS AND MISSING VALUES */
. global controls "thirties fourties fifties sixties seventies income2quartile income3quartile income4quartile incomenoanswer female highschool college noreligion christiannotcatholic catholic fulltimeworker parttimeworker unemployed selfemployed outofLF goodhealth white black latino asian whitecollar bluecollar serviceworker"
{txt}
{com}. 
. global mv_controls
{txt}
{com}. foreach y of global controls {c -(}
{txt}  2{com}.         global mv_controls $mv_controls mv_`y'
{txt}  3{com}.         gen mv_`y' = (`y' == .)
{txt}  4{com}.         replace `y' = 0 if `y' == .
{txt}  5{com}. {c )-}
{txt}(0 real changes made)
(0 real changes made)
(0 real changes made)
(0 real changes made)
(0 real changes made)
(0 real changes made)
(0 real changes made)
(0 real changes made)
(3,016 real changes made)
(0 real changes made)
(1,361 real changes made)
(1,361 real changes made)
(1,124 real changes made)
(1,124 real changes made)
(1,124 real changes made)
(11,592 real changes made)
(20,673 real changes made)
(11,592 real changes made)
(21,100 real changes made)
(11,592 real changes made)
(11 real changes made)
(16,534 real changes made)
(16,534 real changes made)
(16,534 real changes made)
(16,534 real changes made)
(14,538 real changes made)
(14,538 real changes made)
(14,538 real changes made)

{com}. 
. foreach var of varlist $controls $mv_controls {c -(}
{txt}  2{com}.         assert `var'!= .
{txt}  3{com}. {c )-}
{txt}
{com}. 
. 
. * Check that treatment is well defined
. foreach var of varlist blame* praisi* bad_* TH* TE* treatc* healthc* econc* {c -(}
{txt}  2{com}.         di "`var'"
{txt}  3{com}.         assert `var'!= .
{txt}  4{com}. {c )-}
blame_gov_health
blame_gov_econ
praising_gov_health
praising_gov_econ
bad_econ_situation
bad_health_situation
TH1
TH2
TH3
TH4
TH1_TE1
TH1_TE2
TH1_TE3
TH1_TE4
TH2_TE1
TH2_TE2
TH2_TE3
TH2_TE4
TH3_TE1
TH3_TE2
TH3_TE3
TH3_TE4
TH4_TE1
TH4_TE2
TH4_TE3
TH4_TE4
TE1
TE2
TE3
TE4
treatc
healthc
econc
{txt}
{com}. 
. decode country, gen(country_str)
{txt}
{com}. 
. **# Table E.2. Descriptive statistics and balance tests. 
. 
. matrix mat1 = J(8,3,.)
{txt}
{com}. 
. la var thirties "Thirties"
{txt}
{com}. la var fourties "Fourties"
{txt}
{com}. la var fifties "Fifties"
{txt}
{com}. la var sixties "Sixties"
{txt}
{com}. la var seventies "Seventies"
{txt}
{com}. la var income2quartile "Income, 2nd quartile"
{txt}
{com}. la var income3quartile "Income, 3rd quartile"
{txt}
{com}. la var income4quartile "Income, 4th quartile"
{txt}
{com}. la var incomenoanswer "Income, no answer"
{txt}
{com}. la var female "Female"
{txt}
{com}. la var highschool "High school degree"
{txt}
{com}. la var college "College degree"
{txt}
{com}. la var noreligion "No religion"
{txt}
{com}. la var christiannotcatholic "Christian, not catholic"
{txt}
{com}. la var catholic "Catholic"
{txt}
{com}. la var fulltimeworker "Full-time worker"
{txt}
{com}. la var parttimeworker "Part-time worker"
{txt}
{com}. la var unemployed "Unemployed"
{txt}
{com}. la var selfemployed "Self-employed"
{txt}
{com}. la var outofLF "Out of labor force"
{txt}
{com}. la var goodhealth "Good health situation"
{txt}
{com}. la var white "White"
{txt}
{com}. la var black "Black"
{txt}
{com}. la var latino "Latinx"
{txt}
{com}. la var asian "Asian origin"
{txt}
{com}. la var whitecollar "White-collar"
{txt}
{com}. la var bluecollar "Blue-collar"
{txt}
{com}. la var serviceworker "Service worker"
{txt}
{com}. 
. global control_for_balancedness $controls  
{txt}
{com}. local num : list sizeof global(control_for_balancedness)
{txt}
{com}. matrix stats = J(`num',4,.)
{txt}
{com}. local pos = 1
{txt}
{com}. local rownames
{txt}
{com}. foreach var of global control_for_balancedness {c -(}
{txt}  2{com}.         local lab: variable label `var' 
{txt}  3{com}.         local rownames `rownames' "`lab'"
{txt}  4{com}.         qui su `var'
{txt}  5{com}.         matrix stats[`pos',1] = `r(mean)'
{txt}  6{com}.         matrix stats[`pos',2] = `r(sd)'
{txt}  7{com}.         qui reg `var' TH1_TE1 TH1_TE2 TH1_TE3 TH1_TE4 TH2_TE1 TH2_TE2 TH2_TE3 TH2_TE4 TH3_TE1 TH3_TE2 TH3_TE3 TH3_TE4 TH4_TE1 TH4_TE2 TH4_TE3 
{txt}  8{com}.         matrix stats[`pos',3] = e(F)
{txt}  9{com}.         matrix stats[`pos',4] = Ftail(e(df_m), e(df_r), e(F))
{txt} 10{com}.         local pos = `pos'+1
{txt} 11{com}. {c )-}
{txt}
{com}. 
. matrix rownames stats = `rownames'
{txt}
{com}. mat colnames stats = "Mean" "sd" "F-stat" "p-value" 
{txt}
{com}. esttab matrix(stats, fmt(3 3 3 3)) using "3_output/2_OA/Tables/TableE2.tex", replace nomtitles
{res}{txt}(output written to {browse  `"3_output/2_OA/Tables/TableE2.tex"'})

{com}. 
. 
. **# Table E.3. Impact on the perceived seriousness of the health and economic consequences of the crisis.
. la var serious_h_csqc "Very serious health consequences"
{txt}
{com}. la var serious_e_csqc "Beliefs on Econ csqc"
{txt}
{com}. la var satis_head "Satisfaction with the head of government"
{txt}
{com}. 
. eststo clear
{txt}
{com}. eststo: reg serious_h_csqc bad_health_situation bad_econ_situation, robust

{txt}Linear regression                               Number of obs     = {res}    22,540
                                                {txt}F(2, 22537)       =  {res}    75.27
                                                {txt}Prob > F          = {res}    0.0000
                                                {txt}R-squared         = {res}    0.0066
                                                {txt}Root MSE          =    {res} .42837

{txt}{hline 21}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 22}{c |}{col 34}    Robust
{col 1}      serious_h_csqc{col 22}{c |} Coefficient{col 34}  std. err.{col 46}      t{col 54}   P>|t|{col 62}     [95% con{col 75}f. interval]
{hline 21}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
bad_health_situation {c |}{col 22}{res}{space 2} .0695158{col 34}{space 2}  .005706{col 45}{space 1}   12.18{col 54}{space 3}0.000{col 62}{space 4} .0583317{col 75}{space 3} .0806999
{txt}{space 2}bad_econ_situation {c |}{col 22}{res}{space 2} .0075001{col 34}{space 2} .0057064{col 45}{space 1}    1.31{col 54}{space 3}0.189{col 62}{space 4}-.0036849{col 75}{space 3} .0186851
{txt}{space 15}_cons {c |}{col 22}{res}{space 2} .2058943{col 34}{space 2} .0047468{col 45}{space 1}   43.38{col 54}{space 3}0.000{col 62}{space 4} .1965902{col 75}{space 3} .2151984
{txt}{hline 21}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{res}{txt}({res}est1{txt} stored)

{com}. get_mean

{txt}added scalar:
                 e(av) =  {res}.244
{txt}
{com}. 
. eststo: reg serious_h_csqc bad_health_situation bad_econ_situation $controls $mv_controls  i.country, robust
{txt}{p 0 6 2}note: {bf:mv_thirties} omitted because of collinearity.{p_end}
{p 0 6 2}note: {bf:mv_fourties} omitted because of collinearity.{p_end}
{p 0 6 2}note: {bf:mv_fifties} omitted because of collinearity.{p_end}
{p 0 6 2}note: {bf:mv_sixties} omitted because of collinearity.{p_end}
{p 0 6 2}note: {bf:mv_seventies} omitted because of collinearity.{p_end}
{p 0 6 2}note: {bf:mv_income2quartile} omitted because of collinearity.{p_end}
{p 0 6 2}note: {bf:mv_income3quartile} omitted because of collinearity.{p_end}
{p 0 6 2}note: {bf:mv_income4quartile} omitted because of collinearity.{p_end}
{p 0 6 2}note: {bf:mv_female} omitted because of collinearity.{p_end}
{p 0 6 2}note: {bf:mv_college} omitted because of collinearity.{p_end}
{p 0 6 2}note: {bf:mv_christiannotcatholic} omitted because of collinearity.{p_end}
{p 0 6 2}note: {bf:mv_catholic} omitted because of collinearity.{p_end}
{p 0 6 2}note: {bf:mv_fulltimeworker} omitted because of collinearity.{p_end}
{p 0 6 2}note: {bf:mv_unemployed} omitted because of collinearity.{p_end}
{p 0 6 2}note: {bf:mv_outofLF} omitted because of collinearity.{p_end}
{p 0 6 2}note: {bf:mv_black} omitted because of collinearity.{p_end}
{p 0 6 2}note: {bf:mv_latino} omitted because of collinearity.{p_end}
{p 0 6 2}note: {bf:mv_asian} omitted because of collinearity.{p_end}
{p 0 6 2}note: {bf:mv_bluecollar} omitted because of collinearity.{p_end}
{p 0 6 2}note: {bf:mv_serviceworker} omitted because of collinearity.{p_end}
{p 0 6 2}note: {bf:4.country} omitted because of collinearity.{p_end}
{p 0 6 2}note: {bf:7.country} omitted because of collinearity.{p_end}
{p 0 6 2}note: {bf:9.country} omitted because of collinearity.{p_end}
{p 0 6 2}note: {bf:11.country} omitted because of collinearity.{p_end}
{p 0 6 2}note: {bf:12.country} omitted because of collinearity.{p_end}

Linear regression                               Number of obs     = {res}    22,540
                                                {txt}F(44, 22495)      =  {res}    63.65
                                                {txt}Prob > F          = {res}    0.0000
                                                {txt}R-squared         = {res}    0.1209
                                                {txt}Root MSE          =    {res} .40335

{txt}{hline 24}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 25}{c |}{col 37}    Robust
{col 1}         serious_h_csqc{col 25}{c |} Coefficient{col 37}  std. err.{col 49}      t{col 57}   P>|t|{col 65}     [95% con{col 78}f. interval]
{hline 24}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 3}bad_health_situation {c |}{col 25}{res}{space 2} .0694196{col 37}{space 2} .0053762{col 48}{space 1}   12.91{col 57}{space 3}0.000{col 65}{space 4} .0588818{col 78}{space 3} .0799574
{txt}{space 5}bad_econ_situation {c |}{col 25}{res}{space 2} .0072293{col 37}{space 2} .0053772{col 48}{space 1}    1.34{col 57}{space 3}0.179{col 65}{space 4}-.0033105{col 78}{space 3}  .017769
{txt}{space 15}thirties {c |}{col 25}{res}{space 2} .0098523{col 37}{space 2} .0097685{col 48}{space 1}    1.01{col 57}{space 3}0.313{col 65}{space 4}-.0092947{col 78}{space 3} .0289992
{txt}{space 15}fourties {c |}{col 25}{res}{space 2} .0128385{col 37}{space 2} .0095747{col 48}{space 1}    1.34{col 57}{space 3}0.180{col 65}{space 4}-.0059286{col 78}{space 3} .0316056
{txt}{space 16}fifties {c |}{col 25}{res}{space 2}  .019964{col 37}{space 2} .0099394{col 48}{space 1}    2.01{col 57}{space 3}0.045{col 65}{space 4}  .000482{col 78}{space 3} .0394459
{txt}{space 16}sixties {c |}{col 25}{res}{space 2} .0125029{col 37}{space 2} .0098841{col 48}{space 1}    1.26{col 57}{space 3}0.206{col 65}{space 4}-.0068706{col 78}{space 3} .0318764
{txt}{space 14}seventies {c |}{col 25}{res}{space 2} .0057144{col 37}{space 2} .0117672{col 48}{space 1}    0.49{col 57}{space 3}0.627{col 65}{space 4}-.0173502{col 78}{space 3}  .028779
{txt}{space 8}income2quartile {c |}{col 25}{res}{space 2}-.0148119{col 37}{space 2} .0081315{col 48}{space 1}   -1.82{col 57}{space 3}0.069{col 65}{space 4}-.0307502{col 78}{space 3} .0011265
{txt}{space 8}income3quartile {c |}{col 25}{res}{space 2}-.0294196{col 37}{space 2} .0082489{col 48}{space 1}   -3.57{col 57}{space 3}0.000{col 65}{space 4}-.0455881{col 78}{space 3}-.0132511
{txt}{space 8}income4quartile {c |}{col 25}{res}{space 2}-.0296717{col 37}{space 2} .0084265{col 48}{space 1}   -3.52{col 57}{space 3}0.000{col 65}{space 4}-.0461882{col 78}{space 3}-.0131553
{txt}{space 9}incomenoanswer {c |}{col 25}{res}{space 2}-.0044046{col 37}{space 2}  .012832{col 48}{space 1}   -0.34{col 57}{space 3}0.731{col 65}{space 4}-.0295561{col 78}{space 3} .0207469
{txt}{space 17}female {c |}{col 25}{res}{space 2} .0241101{col 37}{space 2} .0054779{col 48}{space 1}    4.40{col 57}{space 3}0.000{col 65}{space 4} .0133731{col 78}{space 3} .0348471
{txt}{space 13}highschool {c |}{col 25}{res}{space 2}-.0157877{col 37}{space 2} .0092972{col 48}{space 1}   -1.70{col 57}{space 3}0.089{col 65}{space 4}-.0340108{col 78}{space 3} .0024354
{txt}{space 16}college {c |}{col 25}{res}{space 2}-.0247916{col 37}{space 2} .0086128{col 48}{space 1}   -2.88{col 57}{space 3}0.004{col 65}{space 4}-.0416733{col 78}{space 3}-.0079099
{txt}{space 13}noreligion {c |}{col 25}{res}{space 2}-.0404315{col 37}{space 2} .0121343{col 48}{space 1}   -3.33{col 57}{space 3}0.001{col 65}{space 4}-.0642155{col 78}{space 3}-.0166474
{txt}{space 3}christiannotcatholic {c |}{col 25}{res}{space 2}-.0403237{col 37}{space 2} .0139603{col 48}{space 1}   -2.89{col 57}{space 3}0.004{col 65}{space 4}-.0676869{col 78}{space 3}-.0129604
{txt}{space 15}catholic {c |}{col 25}{res}{space 2}-.0176395{col 37}{space 2} .0123766{col 48}{space 1}   -1.43{col 57}{space 3}0.154{col 65}{space 4}-.0418986{col 78}{space 3} .0066196
{txt}{space 9}fulltimeworker {c |}{col 25}{res}{space 2} .0113257{col 37}{space 2} .0132884{col 48}{space 1}    0.85{col 57}{space 3}0.394{col 65}{space 4}-.0147205{col 78}{space 3}  .037372
{txt}{space 9}parttimeworker {c |}{col 25}{res}{space 2} .0175405{col 37}{space 2} .0391824{col 48}{space 1}    0.45{col 57}{space 3}0.654{col 65}{space 4}-.0592597{col 78}{space 3} .0943408
{txt}{space 13}unemployed {c |}{col 25}{res}{space 2}-.0074861{col 37}{space 2}  .021469{col 48}{space 1}   -0.35{col 57}{space 3}0.727{col 65}{space 4}-.0495669{col 78}{space 3} .0345946
{txt}{space 11}selfemployed {c |}{col 25}{res}{space 2}-.0387089{col 37}{space 2} .0569687{col 48}{space 1}   -0.68{col 57}{space 3}0.497{col 65}{space 4}-.1503716{col 78}{space 3} .0729538
{txt}{space 16}outofLF {c |}{col 25}{res}{space 2} .0031121{col 37}{space 2} .0142936{col 48}{space 1}    0.22{col 57}{space 3}0.828{col 65}{space 4}-.0249043{col 78}{space 3} .0311285
{txt}{space 13}goodhealth {c |}{col 25}{res}{space 2}-.0356548{col 37}{space 2} .0059459{col 48}{space 1}   -6.00{col 57}{space 3}0.000{col 65}{space 4}-.0473092{col 78}{space 3}-.0240004
{txt}{space 18}white {c |}{col 25}{res}{space 2} .0289145{col 37}{space 2} .0655429{col 48}{space 1}    0.44{col 57}{space 3}0.659{col 65}{space 4}-.0995542{col 78}{space 3} .1573831
{txt}{space 18}black {c |}{col 25}{res}{space 2} .2118857{col 37}{space 2}  .072059{col 48}{space 1}    2.94{col 57}{space 3}0.003{col 65}{space 4}  .070645{col 78}{space 3} .3531264
{txt}{space 17}latino {c |}{col 25}{res}{space 2} .1341049{col 37}{space 2} .0795349{col 48}{space 1}    1.69{col 57}{space 3}0.092{col 65}{space 4}-.0217891{col 78}{space 3} .2899989
{txt}{space 18}asian {c |}{col 25}{res}{space 2} .0011963{col 37}{space 2} .0793613{col 48}{space 1}    0.02{col 57}{space 3}0.988{col 65}{space 4}-.1543573{col 78}{space 3} .1567499
{txt}{space 12}whitecollar {c |}{col 25}{res}{space 2} .0164782{col 37}{space 2} .0137232{col 48}{space 1}    1.20{col 57}{space 3}0.230{col 65}{space 4}-.0104202{col 78}{space 3} .0433767
{txt}{space 13}bluecollar {c |}{col 25}{res}{space 2}-.0016771{col 37}{space 2} .0138635{col 48}{space 1}   -0.12{col 57}{space 3}0.904{col 65}{space 4}-.0288505{col 78}{space 3} .0254964
{txt}{space 10}serviceworker {c |}{col 25}{res}{space 2}-.0216653{col 37}{space 2} .0109996{col 48}{space 1}   -1.97{col 57}{space 3}0.049{col 65}{space 4}-.0432253{col 78}{space 3}-.0001052
{txt}{space 12}mv_thirties {c |}{col 25}{res}{space 2}        0{col 37}{txt}  (omitted)
{space 12}mv_fourties {c |}{col 25}{res}{space 2}        0{col 37}{txt}  (omitted)
{space 13}mv_fifties {c |}{col 25}{res}{space 2}        0{col 37}{txt}  (omitted)
{space 13}mv_sixties {c |}{col 25}{res}{space 2}        0{col 37}{txt}  (omitted)
{space 11}mv_seventies {c |}{col 25}{res}{space 2}        0{col 37}{txt}  (omitted)
{space 5}mv_income2quartile {c |}{col 25}{res}{space 2}        0{col 37}{txt}  (omitted)
{space 5}mv_income3quartile {c |}{col 25}{res}{space 2}        0{col 37}{txt}  (omitted)
{space 5}mv_income4quartile {c |}{col 25}{res}{space 2}        0{col 37}{txt}  (omitted)
{space 6}mv_incomenoanswer {c |}{col 25}{res}{space 2} .1580699{col 37}{space 2} .0193606{col 48}{space 1}    8.16{col 57}{space 3}0.000{col 65}{space 4} .1201217{col 78}{space 3}  .196018
{txt}{space 14}mv_female {c |}{col 25}{res}{space 2}        0{col 37}{txt}  (omitted)
{space 10}mv_highschool {c |}{col 25}{res}{space 2}-.0067908{col 37}{space 2} .0218884{col 48}{space 1}   -0.31{col 57}{space 3}0.756{col 65}{space 4}-.0496937{col 78}{space 3}  .036112
{txt}{space 13}mv_college {c |}{col 25}{res}{space 2}        0{col 37}{txt}  (omitted)
{space 10}mv_noreligion {c |}{col 25}{res}{space 2} .0150621{col 37}{space 2} .0483004{col 48}{space 1}    0.31{col 57}{space 3}0.755{col 65}{space 4}-.0796101{col 78}{space 3} .1097342
{txt}mv_christiannotcatholic {c |}{col 25}{res}{space 2}        0{col 37}{txt}  (omitted)
{space 12}mv_catholic {c |}{col 25}{res}{space 2}        0{col 37}{txt}  (omitted)
{space 6}mv_fulltimeworker {c |}{col 25}{res}{space 2}        0{col 37}{txt}  (omitted)
{space 6}mv_parttimeworker {c |}{col 25}{res}{space 2} .0018549{col 37}{space 2} .0287281{col 48}{space 1}    0.06{col 57}{space 3}0.949{col 65}{space 4}-.0544541{col 78}{space 3} .0581639
{txt}{space 10}mv_unemployed {c |}{col 25}{res}{space 2}        0{col 37}{txt}  (omitted)
{space 8}mv_selfemployed {c |}{col 25}{res}{space 2}-.2224433{col 37}{space 2} .0311718{col 48}{space 1}   -7.14{col 57}{space 3}0.000{col 65}{space 4}-.2835422{col 78}{space 3}-.1613445
{txt}{space 13}mv_outofLF {c |}{col 25}{res}{space 2}        0{col 37}{txt}  (omitted)
{space 10}mv_goodhealth {c |}{col 25}{res}{space 2} .0993911{col 37}{space 2} .1514117{col 48}{space 1}    0.66{col 57}{space 3}0.512{col 65}{space 4}-.1973864{col 78}{space 3} .3961685
{txt}{space 15}mv_white {c |}{col 25}{res}{space 2} -.079868{col 37}{space 2} .0659263{col 48}{space 1}   -1.21{col 57}{space 3}0.226{col 65}{space 4}-.2090881{col 78}{space 3}  .049352
{txt}{space 15}mv_black {c |}{col 25}{res}{space 2}        0{col 37}{txt}  (omitted)
{space 14}mv_latino {c |}{col 25}{res}{space 2}        0{col 37}{txt}  (omitted)
{space 15}mv_asian {c |}{col 25}{res}{space 2}        0{col 37}{txt}  (omitted)
{space 9}mv_whitecollar {c |}{col 25}{res}{space 2}-.2500011{col 37}{space 2} .0291429{col 48}{space 1}   -8.58{col 57}{space 3}0.000{col 65}{space 4}-.3071232{col 78}{space 3} -.192879
{txt}{space 10}mv_bluecollar {c |}{col 25}{res}{space 2}        0{col 37}{txt}  (omitted)
{space 7}mv_serviceworker {c |}{col 25}{res}{space 2}        0{col 37}{txt}  (omitted)
{space 23} {c |}
{space 16}country {c |}
{space 15}Austria  {c |}{col 25}{res}{space 2}-.4064495{col 37}{space 2} .0714694{col 48}{space 1}   -5.69{col 57}{space 3}0.000{col 65}{space 4}-.5465345{col 78}{space 3}-.2663645
{txt}{space 16}Brazil  {c |}{col 25}{res}{space 2} .2383704{col 37}{space 2} .0302794{col 48}{space 1}    7.87{col 57}{space 3}0.000{col 65}{space 4} .1790207{col 78}{space 3} .2977201
{txt}{space 16}France  {c |}{col 25}{res}{space 2}        0{col 37}{txt}  (omitted)
{space 15}Germany  {c |}{col 25}{res}{space 2}-.3841564{col 37}{space 2} .0712645{col 48}{space 1}   -5.39{col 57}{space 3}0.000{col 65}{space 4}-.5238398{col 78}{space 3} -.244473
{txt}{space 17}Italy  {c |}{col 25}{res}{space 2}-.3757039{col 37}{space 2} .0858811{col 48}{space 1}   -4.37{col 57}{space 3}0.000{col 65}{space 4}-.5440369{col 78}{space 3} -.207371
{txt}{space 11}New_Zealand  {c |}{col 25}{res}{space 2}        0{col 37}{txt}  (omitted)
{space 16}Poland  {c |}{col 25}{res}{space 2}-.2553007{col 37}{space 2} .0292608{col 48}{space 1}   -8.73{col 57}{space 3}0.000{col 65}{space 4}-.3126538{col 78}{space 3}-.1979475
{txt}{space 17}Spain  {c |}{col 25}{res}{space 2}        0{col 37}{txt}  (omitted)
{space 16}Sweden  {c |}{col 25}{res}{space 2}-.1419335{col 37}{space 2} .0295289{col 48}{space 1}   -4.81{col 57}{space 3}0.000{col 65}{space 4}-.1998123{col 78}{space 3}-.0840548
{txt}{space 20}UK  {c |}{col 25}{res}{space 2}        0{col 37}{txt}  (omitted)
{space 20}US  {c |}{col 25}{res}{space 2}        0{col 37}{txt}  (omitted)
{space 23} {c |}
{space 18}_cons {c |}{col 25}{res}{space 2} .7407875{col 37}{space 2} .0740799{col 48}{space 1}   10.00{col 57}{space 3}0.000{col 65}{space 4} .5955858{col 78}{space 3} .8859892
{txt}{hline 24}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{res}{txt}({res}est2{txt} stored)

{com}. estadd local Controls "X", replace

{txt}added macro:
           e(Controls) : "{res:X}"

{com}. estadd local Country_FE "X", replace

{txt}added macro:
         e(Country_FE) : "{res:X}"

{com}. get_mean

{txt}added scalar:
                 e(av) =  {res}.244
{txt}
{com}. 
. eststo: reg serious_e_csqc bad_health_situation bad_econ_situation, robust

{txt}Linear regression                               Number of obs     = {res}    22,538
                                                {txt}F(2, 22535)       =  {res}    91.29
                                                {txt}Prob > F          = {res}    0.0000
                                                {txt}R-squared         = {res}    0.0080
                                                {txt}Root MSE          =    {res} .48077

{txt}{hline 21}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 22}{c |}{col 34}    Robust
{col 1}      serious_e_csqc{col 22}{c |} Coefficient{col 34}  std. err.{col 46}      t{col 54}   P>|t|{col 62}     [95% con{col 75}f. interval]
{hline 21}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
bad_health_situation {c |}{col 22}{res}{space 2} .0239962{col 34}{space 2} .0064048{col 45}{space 1}    3.75{col 54}{space 3}0.000{col 62}{space 4} .0114423{col 75}{space 3} .0365501
{txt}{space 2}bad_econ_situation {c |}{col 22}{res}{space 2} .0830844{col 34}{space 2} .0064039{col 45}{space 1}   12.97{col 54}{space 3}0.000{col 62}{space 4} .0705322{col 75}{space 3} .0956365
{txt}{space 15}_cons {c |}{col 22}{res}{space 2} .3158583{col 34}{space 2} .0054411{col 45}{space 1}   58.05{col 54}{space 3}0.000{col 62}{space 4} .3051934{col 75}{space 3} .3265233
{txt}{hline 21}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{res}{txt}({res}est3{txt} stored)

{com}. get_mean

{txt}added scalar:
                 e(av) =  {res}.37
{txt}
{com}. 
. eststo: reg serious_e_csqc bad_health_situation bad_econ_situation $controls $mv_controls  i.country, robust
{txt}{p 0 6 2}note: {bf:mv_thirties} omitted because of collinearity.{p_end}
{p 0 6 2}note: {bf:mv_fourties} omitted because of collinearity.{p_end}
{p 0 6 2}note: {bf:mv_fifties} omitted because of collinearity.{p_end}
{p 0 6 2}note: {bf:mv_sixties} omitted because of collinearity.{p_end}
{p 0 6 2}note: {bf:mv_seventies} omitted because of collinearity.{p_end}
{p 0 6 2}note: {bf:mv_income2quartile} omitted because of collinearity.{p_end}
{p 0 6 2}note: {bf:mv_income3quartile} omitted because of collinearity.{p_end}
{p 0 6 2}note: {bf:mv_income4quartile} omitted because of collinearity.{p_end}
{p 0 6 2}note: {bf:mv_female} omitted because of collinearity.{p_end}
{p 0 6 2}note: {bf:mv_college} omitted because of collinearity.{p_end}
{p 0 6 2}note: {bf:mv_christiannotcatholic} omitted because of collinearity.{p_end}
{p 0 6 2}note: {bf:mv_catholic} omitted because of collinearity.{p_end}
{p 0 6 2}note: {bf:mv_fulltimeworker} omitted because of collinearity.{p_end}
{p 0 6 2}note: {bf:mv_unemployed} omitted because of collinearity.{p_end}
{p 0 6 2}note: {bf:mv_outofLF} omitted because of collinearity.{p_end}
{p 0 6 2}note: {bf:mv_black} omitted because of collinearity.{p_end}
{p 0 6 2}note: {bf:mv_latino} omitted because of collinearity.{p_end}
{p 0 6 2}note: {bf:mv_asian} omitted because of collinearity.{p_end}
{p 0 6 2}note: {bf:mv_bluecollar} omitted because of collinearity.{p_end}
{p 0 6 2}note: {bf:mv_serviceworker} omitted because of collinearity.{p_end}
{p 0 6 2}note: {bf:4.country} omitted because of collinearity.{p_end}
{p 0 6 2}note: {bf:7.country} omitted because of collinearity.{p_end}
{p 0 6 2}note: {bf:9.country} omitted because of collinearity.{p_end}
{p 0 6 2}note: {bf:11.country} omitted because of collinearity.{p_end}
{p 0 6 2}note: {bf:12.country} omitted because of collinearity.{p_end}

Linear regression                               Number of obs     = {res}    22,538
                                                {txt}F(44, 22493)      =  {res}    40.05
                                                {txt}Prob > F          = {res}    0.0000
                                                {txt}R-squared         = {res}    0.0710
                                                {txt}Root MSE          =    {res}  .4657

{txt}{hline 24}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 25}{c |}{col 37}    Robust
{col 1}         serious_e_csqc{col 25}{c |} Coefficient{col 37}  std. err.{col 49}      t{col 57}   P>|t|{col 65}     [95% con{col 78}f. interval]
{hline 24}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 3}bad_health_situation {c |}{col 25}{res}{space 2} .0236669{col 37}{space 2} .0062081{col 48}{space 1}    3.81{col 57}{space 3}0.000{col 65}{space 4} .0114985{col 78}{space 3} .0358353
{txt}{space 5}bad_econ_situation {c |}{col 25}{res}{space 2} .0835033{col 37}{space 2} .0062094{col 48}{space 1}   13.45{col 57}{space 3}0.000{col 65}{space 4} .0713324{col 78}{space 3} .0956743
{txt}{space 15}thirties {c |}{col 25}{res}{space 2} .0197299{col 37}{space 2}   .01105{col 48}{space 1}    1.79{col 57}{space 3}0.074{col 65}{space 4}-.0019288{col 78}{space 3} .0413886
{txt}{space 15}fourties {c |}{col 25}{res}{space 2} .0553704{col 37}{space 2} .0109756{col 48}{space 1}    5.04{col 57}{space 3}0.000{col 65}{space 4} .0338576{col 78}{space 3} .0768833
{txt}{space 16}fifties {c |}{col 25}{res}{space 2} .0712132{col 37}{space 2} .0113323{col 48}{space 1}    6.28{col 57}{space 3}0.000{col 65}{space 4} .0490011{col 78}{space 3} .0934253
{txt}{space 16}sixties {c |}{col 25}{res}{space 2} .0641972{col 37}{space 2} .0113239{col 48}{space 1}    5.67{col 57}{space 3}0.000{col 65}{space 4} .0420016{col 78}{space 3} .0863929
{txt}{space 14}seventies {c |}{col 25}{res}{space 2} .0767017{col 37}{space 2} .0135131{col 48}{space 1}    5.68{col 57}{space 3}0.000{col 65}{space 4} .0502152{col 78}{space 3} .1031883
{txt}{space 8}income2quartile {c |}{col 25}{res}{space 2}-.0128089{col 37}{space 2}  .009213{col 48}{space 1}   -1.39{col 57}{space 3}0.164{col 65}{space 4}-.0308671{col 78}{space 3} .0052493
{txt}{space 8}income3quartile {c |}{col 25}{res}{space 2}-.0173548{col 37}{space 2} .0094917{col 48}{space 1}   -1.83{col 57}{space 3}0.068{col 65}{space 4}-.0359593{col 78}{space 3} .0012497
{txt}{space 8}income4quartile {c |}{col 25}{res}{space 2}-.0113933{col 37}{space 2}  .009739{col 48}{space 1}   -1.17{col 57}{space 3}0.242{col 65}{space 4}-.0304824{col 78}{space 3} .0076957
{txt}{space 9}incomenoanswer {c |}{col 25}{res}{space 2}-.0089931{col 37}{space 2} .0146307{col 48}{space 1}   -0.61{col 57}{space 3}0.539{col 65}{space 4}-.0376702{col 78}{space 3}  .019684
{txt}{space 17}female {c |}{col 25}{res}{space 2} .0066521{col 37}{space 2}  .006343{col 48}{space 1}    1.05{col 57}{space 3}0.294{col 65}{space 4}-.0057807{col 78}{space 3} .0190848
{txt}{space 13}highschool {c |}{col 25}{res}{space 2} .0053393{col 37}{space 2} .0107103{col 48}{space 1}    0.50{col 57}{space 3}0.618{col 65}{space 4}-.0156535{col 78}{space 3} .0263322
{txt}{space 16}college {c |}{col 25}{res}{space 2} .0098808{col 37}{space 2} .0100581{col 48}{space 1}    0.98{col 57}{space 3}0.326{col 65}{space 4}-.0098337{col 78}{space 3} .0295954
{txt}{space 13}noreligion {c |}{col 25}{res}{space 2} .0000691{col 37}{space 2} .0133238{col 48}{space 1}    0.01{col 57}{space 3}0.996{col 65}{space 4}-.0260465{col 78}{space 3} .0261847
{txt}{space 3}christiannotcatholic {c |}{col 25}{res}{space 2}-.0131499{col 37}{space 2} .0151114{col 48}{space 1}   -0.87{col 57}{space 3}0.384{col 65}{space 4}-.0427694{col 78}{space 3} .0164695
{txt}{space 15}catholic {c |}{col 25}{res}{space 2} .0226922{col 37}{space 2} .0136391{col 48}{space 1}    1.66{col 57}{space 3}0.096{col 65}{space 4}-.0040413{col 78}{space 3} .0494257
{txt}{space 9}fulltimeworker {c |}{col 25}{res}{space 2} .0151788{col 37}{space 2} .0160793{col 48}{space 1}    0.94{col 57}{space 3}0.345{col 65}{space 4}-.0163377{col 78}{space 3} .0466954
{txt}{space 9}parttimeworker {c |}{col 25}{res}{space 2} .0480581{col 37}{space 2} .0396255{col 48}{space 1}    1.21{col 57}{space 3}0.225{col 65}{space 4}-.0296107{col 78}{space 3} .1257269
{txt}{space 13}unemployed {c |}{col 25}{res}{space 2} .0505985{col 37}{space 2} .0249739{col 48}{space 1}    2.03{col 57}{space 3}0.043{col 65}{space 4} .0016479{col 78}{space 3}  .099549
{txt}{space 11}selfemployed {c |}{col 25}{res}{space 2} .0113704{col 37}{space 2} .0595056{col 48}{space 1}    0.19{col 57}{space 3}0.848{col 65}{space 4}-.1052647{col 78}{space 3} .1280055
{txt}{space 16}outofLF {c |}{col 25}{res}{space 2} .0281293{col 37}{space 2} .0172688{col 48}{space 1}    1.63{col 57}{space 3}0.103{col 65}{space 4}-.0057189{col 78}{space 3} .0619774
{txt}{space 13}goodhealth {c |}{col 25}{res}{space 2} .0065144{col 37}{space 2} .0067437{col 48}{space 1}    0.97{col 57}{space 3}0.334{col 65}{space 4}-.0067037{col 78}{space 3} .0197326
{txt}{space 18}white {c |}{col 25}{res}{space 2}-.0046134{col 37}{space 2} .0646974{col 48}{space 1}   -0.07{col 57}{space 3}0.943{col 65}{space 4}-.1314247{col 78}{space 3} .1221979
{txt}{space 18}black {c |}{col 25}{res}{space 2} .1150779{col 37}{space 2} .0715466{col 48}{space 1}    1.61{col 57}{space 3}0.108{col 65}{space 4}-.0251584{col 78}{space 3} .2553143
{txt}{space 17}latino {c |}{col 25}{res}{space 2} .0272749{col 37}{space 2} .0786108{col 48}{space 1}    0.35{col 57}{space 3}0.729{col 65}{space 4}-.1268077{col 78}{space 3} .1813575
{txt}{space 18}asian {c |}{col 25}{res}{space 2}-.0317077{col 37}{space 2} .0779786{col 48}{space 1}   -0.41{col 57}{space 3}0.684{col 65}{space 4}-.1845512{col 78}{space 3} .1211357
{txt}{space 12}whitecollar {c |}{col 25}{res}{space 2}  .027826{col 37}{space 2} .0157685{col 48}{space 1}    1.76{col 57}{space 3}0.078{col 65}{space 4}-.0030814{col 78}{space 3} .0587333
{txt}{space 13}bluecollar {c |}{col 25}{res}{space 2} .0332032{col 37}{space 2} .0163841{col 48}{space 1}    2.03{col 57}{space 3}0.043{col 65}{space 4} .0010892{col 78}{space 3} .0653172
{txt}{space 10}serviceworker {c |}{col 25}{res}{space 2} .0248848{col 37}{space 2} .0130959{col 48}{space 1}    1.90{col 57}{space 3}0.057{col 65}{space 4}-.0007841{col 78}{space 3} .0505536
{txt}{space 12}mv_thirties {c |}{col 25}{res}{space 2}        0{col 37}{txt}  (omitted)
{space 12}mv_fourties {c |}{col 25}{res}{space 2}        0{col 37}{txt}  (omitted)
{space 13}mv_fifties {c |}{col 25}{res}{space 2}        0{col 37}{txt}  (omitted)
{space 13}mv_sixties {c |}{col 25}{res}{space 2}        0{col 37}{txt}  (omitted)
{space 11}mv_seventies {c |}{col 25}{res}{space 2}        0{col 37}{txt}  (omitted)
{space 5}mv_income2quartile {c |}{col 25}{res}{space 2}        0{col 37}{txt}  (omitted)
{space 5}mv_income3quartile {c |}{col 25}{res}{space 2}        0{col 37}{txt}  (omitted)
{space 5}mv_income4quartile {c |}{col 25}{res}{space 2}        0{col 37}{txt}  (omitted)
{space 6}mv_incomenoanswer {c |}{col 25}{res}{space 2} .1723449{col 37}{space 2} .0217306{col 48}{space 1}    7.93{col 57}{space 3}0.000{col 65}{space 4} .1297514{col 78}{space 3} .2149384
{txt}{space 14}mv_female {c |}{col 25}{res}{space 2}        0{col 37}{txt}  (omitted)
{space 10}mv_highschool {c |}{col 25}{res}{space 2} .0192347{col 37}{space 2} .0260352{col 48}{space 1}    0.74{col 57}{space 3}0.460{col 65}{space 4}-.0317961{col 78}{space 3} .0702654
{txt}{space 13}mv_college {c |}{col 25}{res}{space 2}        0{col 37}{txt}  (omitted)
{space 10}mv_noreligion {c |}{col 25}{res}{space 2} .0255176{col 37}{space 2}  .046581{col 48}{space 1}    0.55{col 57}{space 3}0.584{col 65}{space 4}-.0657843{col 78}{space 3} .1168196
{txt}mv_christiannotcatholic {c |}{col 25}{res}{space 2}        0{col 37}{txt}  (omitted)
{space 12}mv_catholic {c |}{col 25}{res}{space 2}        0{col 37}{txt}  (omitted)
{space 6}mv_fulltimeworker {c |}{col 25}{res}{space 2}        0{col 37}{txt}  (omitted)
{space 6}mv_parttimeworker {c |}{col 25}{res}{space 2} .0369645{col 37}{space 2} .0306004{col 48}{space 1}    1.21{col 57}{space 3}0.227{col 65}{space 4}-.0230145{col 78}{space 3} .0969435
{txt}{space 10}mv_unemployed {c |}{col 25}{res}{space 2}        0{col 37}{txt}  (omitted)
{space 8}mv_selfemployed {c |}{col 25}{res}{space 2}-.3200292{col 37}{space 2} .0328001{col 48}{space 1}   -9.76{col 57}{space 3}0.000{col 65}{space 4}-.3843197{col 78}{space 3}-.2557387
{txt}{space 13}mv_outofLF {c |}{col 25}{res}{space 2}        0{col 37}{txt}  (omitted)
{space 10}mv_goodhealth {c |}{col 25}{res}{space 2} .1905209{col 37}{space 2} .1400602{col 48}{space 1}    1.36{col 57}{space 3}0.174{col 65}{space 4}-.0840067{col 78}{space 3} .4650486
{txt}{space 15}mv_white {c |}{col 25}{res}{space 2} .0044335{col 37}{space 2} .0652637{col 48}{space 1}    0.07{col 57}{space 3}0.946{col 65}{space 4}-.1234879{col 78}{space 3} .1323549
{txt}{space 15}mv_black {c |}{col 25}{res}{space 2}        0{col 37}{txt}  (omitted)
{space 14}mv_latino {c |}{col 25}{res}{space 2}        0{col 37}{txt}  (omitted)
{space 15}mv_asian {c |}{col 25}{res}{space 2}        0{col 37}{txt}  (omitted)
{space 9}mv_whitecollar {c |}{col 25}{res}{space 2}-.2380558{col 37}{space 2} .0335014{col 48}{space 1}   -7.11{col 57}{space 3}0.000{col 65}{space 4} -.303721{col 78}{space 3}-.1723907
{txt}{space 10}mv_bluecollar {c |}{col 25}{res}{space 2}        0{col 37}{txt}  (omitted)
{space 7}mv_serviceworker {c |}{col 25}{res}{space 2}        0{col 37}{txt}  (omitted)
{space 23} {c |}
{space 16}country {c |}
{space 15}Austria  {c |}{col 25}{res}{space 2}-.2881727{col 37}{space 2} .0730075{col 48}{space 1}   -3.95{col 57}{space 3}0.000{col 65}{space 4}-.4312725{col 78}{space 3}-.1450729
{txt}{space 16}Brazil  {c |}{col 25}{res}{space 2} .0824037{col 37}{space 2} .0335215{col 48}{space 1}    2.46{col 57}{space 3}0.014{col 65}{space 4} .0166992{col 78}{space 3} .1481083
{txt}{space 16}France  {c |}{col 25}{res}{space 2}        0{col 37}{txt}  (omitted)
{space 15}Germany  {c |}{col 25}{res}{space 2}-.3009283{col 37}{space 2} .0722897{col 48}{space 1}   -4.16{col 57}{space 3}0.000{col 65}{space 4}-.4426212{col 78}{space 3}-.1592354
{txt}{space 17}Italy  {c |}{col 25}{res}{space 2} -.088081{col 37}{space 2} .0860067{col 48}{space 1}   -1.02{col 57}{space 3}0.306{col 65}{space 4}  -.25666{col 78}{space 3} .0804981
{txt}{space 11}New_Zealand  {c |}{col 25}{res}{space 2}        0{col 37}{txt}  (omitted)
{space 16}Poland  {c |}{col 25}{res}{space 2}-.2550236{col 37}{space 2} .0336767{col 48}{space 1}   -7.57{col 57}{space 3}0.000{col 65}{space 4}-.3210322{col 78}{space 3} -.189015
{txt}{space 17}Spain  {c |}{col 25}{res}{space 2}        0{col 37}{txt}  (omitted)
{space 16}Sweden  {c |}{col 25}{res}{space 2}-.2837098{col 37}{space 2} .0327355{col 48}{space 1}   -8.67{col 57}{space 3}0.000{col 65}{space 4}-.3478736{col 78}{space 3} -.219546
{txt}{space 20}UK  {c |}{col 25}{res}{space 2}        0{col 37}{txt}  (omitted)
{space 20}US  {c |}{col 25}{res}{space 2}        0{col 37}{txt}  (omitted)
{space 23} {c |}
{space 18}_cons {c |}{col 25}{res}{space 2} .6943461{col 37}{space 2} .0753686{col 48}{space 1}    9.21{col 57}{space 3}0.000{col 65}{space 4} .5466183{col 78}{space 3} .8420739
{txt}{hline 24}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{res}{txt}({res}est4{txt} stored)

{com}. estadd local Controls "X", replace

{txt}added macro:
           e(Controls) : "{res:X}"

{com}. estadd local Country_FE "X", replace

{txt}added macro:
         e(Country_FE) : "{res:X}"

{com}. get_mean

{txt}added scalar:
                 e(av) =  {res}.37
{txt}
{com}. 
. esttab using "3_output/2_OA/Tables/TableE3.tex", r2 dep nocons ///
>         keep(_cons bad_health_situation bad_econ_situation ) ///
>         order(_cons bad_health_situation bad_econ_situation ) ///
>         label replace   cells(b(star fmt(%15.3fc)) se(par fmt(%15.3fc))) interaction(" $/times$ ") ///
>         star(* 0.10 ** 0.05 *** 0.01) ///
>         stats(Controls Country_FE N r2 av, fmt(%15.0fc %15.0fc %15.0fc %6.3f %6.3f ) layout(@ @ @ @ @) ///
>         labels("Individual controls" "Country FE" Observations R2 "Outcome mean")) ///
>         collabels(none) mlabels(none) ///
>         mgroups("Very serious health consequences" "Very serious economic consequences" , pattern(1 0 1 0 0 0 ) prefix(\multicolumn{c -(}@span{c )-}{c -(}c{c )-}{c -(}) suffix({c )-}) span erepeat(\cmidrule(lr){c -(}@span{c )-})) 
{res}{txt}(output written to {browse  `"3_output/2_OA/Tables/TableE3.tex"'})

{com}. 
.         
. **# Table E.4. Impact on satisfaction with the government's response to the crisis.             
. eststo clear
{txt}
{com}. eststo: reg eval_sant bad_health_situation blame_gov_health praising_gov_health bad_econ_situation blame_gov_econ praising_gov_econ, robust

{txt}Linear regression                               Number of obs     = {res}    22,541
                                                {txt}F(6, 22534)       =  {res}     8.19
                                                {txt}Prob > F          = {res}    0.0000
                                                {txt}R-squared         = {res}    0.0022
                                                {txt}Root MSE          =    {res}  .2695

{txt}{hline 21}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 22}{c |}{col 34}    Robust
{col 1}           eval_sant{col 22}{c |} Coefficient{col 34}  std. err.{col 46}      t{col 54}   P>|t|{col 62}     [95% con{col 75}f. interval]
{hline 21}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
bad_health_situation {c |}{col 22}{res}{space 2}-.0060569{col 34}{space 2}  .005096{col 45}{space 1}   -1.19{col 54}{space 3}0.235{col 62}{space 4}-.0160455{col 75}{space 3} .0039317
{txt}{space 4}blame_gov_health {c |}{col 22}{res}{space 2}-.0164537{col 34}{space 2} .0051161{col 45}{space 1}   -3.22{col 54}{space 3}0.001{col 62}{space 4}-.0264817{col 75}{space 3}-.0064258
{txt}{space 1}praising_gov_health {c |}{col 22}{res}{space 2} .0116742{col 34}{space 2} .0050383{col 45}{space 1}    2.32{col 54}{space 3}0.021{col 62}{space 4} .0017988{col 75}{space 3} .0215497
{txt}{space 2}bad_econ_situation {c |}{col 22}{res}{space 2}-.0013167{col 34}{space 2} .0050862{col 45}{space 1}   -0.26{col 54}{space 3}0.796{col 62}{space 4} -.011286{col 75}{space 3} .0086525
{txt}{space 6}blame_gov_econ {c |}{col 22}{res}{space 2} .0000752{col 34}{space 2} .0050917{col 45}{space 1}    0.01{col 54}{space 3}0.988{col 62}{space 4}-.0099048{col 75}{space 3} .0100552
{txt}{space 3}praising_gov_econ {c |}{col 22}{res}{space 2} .0041394{col 34}{space 2} .0050626{col 45}{space 1}    0.82{col 54}{space 3}0.414{col 62}{space 4}-.0057836{col 75}{space 3} .0140625
{txt}{space 15}_cons {c |}{col 22}{res}{space 2} .5126664{col 34}{space 2} .0047476{col 45}{space 1}  107.98{col 54}{space 3}0.000{col 62}{space 4} .5033607{col 75}{space 3} .5219721
{txt}{hline 21}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{res}{txt}({res}est1{txt} stored)

{com}. mylincom3

{p 0 7}{space 1}{text:( 1)}{space 1} {res}blame_gov_health - praising_gov_health = 0{p_end}

{txt}{hline 13}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 1}   eval_sant{col 14}{c |} Coefficient{col 26}  Std. err.{col 38}      t{col 46}   P>|t|{col 54}     [95% con{col 67}f. interval]
{hline 13}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 9}(1) {c |}{col 14}{res}{space 2} -.028128{col 26}{space 2} .0071805{col 37}{space 1}   -3.92{col 46}{space 3}0.000{col 54}{space 4}-.0422023{col 67}{space 3}-.0140536
{txt}{hline 13}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}

added macro:
             e(blameh) : "{res:-0.028***}"

added scalar:
           e(blamehsd) =  {res}.00718055

{p 0 7}{space 1}{text:( 1)}{space 1} blame_gov_econ - praising_gov_econ = 0{p_end}

{txt}{hline 13}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 1}   eval_sant{col 14}{c |} Coefficient{col 26}  Std. err.{col 38}      t{col 46}   P>|t|{col 54}     [95% con{col 67}f. interval]
{hline 13}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 9}(1) {c |}{col 14}{res}{space 2}-.0040642{col 26}{space 2} .0071801{col 37}{space 1}   -0.57{col 46}{space 3}0.571{col 54}{space 4}-.0181376{col 67}{space 3} .0100092
{txt}{hline 13}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}

added macro:
             e(blamee) : "{res:-0.004}"

added scalar:
           e(seblamee) =  {res}.00718006
{txt}
{com}. get_mean

{txt}added scalar:
                 e(av) =  {res}.509
{txt}
{com}. 
. eststo: reg eval_sant bad_health_situation blame_gov_health praising_gov_health bad_econ_situation blame_gov_econ praising_gov_econ $controls $mv_controls  i.country, robust
{txt}{p 0 6 2}note: {bf:mv_thirties} omitted because of collinearity.{p_end}
{p 0 6 2}note: {bf:mv_fourties} omitted because of collinearity.{p_end}
{p 0 6 2}note: {bf:mv_fifties} omitted because of collinearity.{p_end}
{p 0 6 2}note: {bf:mv_sixties} omitted because of collinearity.{p_end}
{p 0 6 2}note: {bf:mv_seventies} omitted because of collinearity.{p_end}
{p 0 6 2}note: {bf:mv_income2quartile} omitted because of collinearity.{p_end}
{p 0 6 2}note: {bf:mv_income3quartile} omitted because of collinearity.{p_end}
{p 0 6 2}note: {bf:mv_income4quartile} omitted because of collinearity.{p_end}
{p 0 6 2}note: {bf:mv_female} omitted because of collinearity.{p_end}
{p 0 6 2}note: {bf:mv_college} omitted because of collinearity.{p_end}
{p 0 6 2}note: {bf:mv_christiannotcatholic} omitted because of collinearity.{p_end}
{p 0 6 2}note: {bf:mv_catholic} omitted because of collinearity.{p_end}
{p 0 6 2}note: {bf:mv_fulltimeworker} omitted because of collinearity.{p_end}
{p 0 6 2}note: {bf:mv_unemployed} omitted because of collinearity.{p_end}
{p 0 6 2}note: {bf:mv_outofLF} omitted because of collinearity.{p_end}
{p 0 6 2}note: {bf:mv_black} omitted because of collinearity.{p_end}
{p 0 6 2}note: {bf:mv_latino} omitted because of collinearity.{p_end}
{p 0 6 2}note: {bf:mv_asian} omitted because of collinearity.{p_end}
{p 0 6 2}note: {bf:mv_bluecollar} omitted because of collinearity.{p_end}
{p 0 6 2}note: {bf:mv_serviceworker} omitted because of collinearity.{p_end}
{p 0 6 2}note: {bf:4.country} omitted because of collinearity.{p_end}
{p 0 6 2}note: {bf:7.country} omitted because of collinearity.{p_end}
{p 0 6 2}note: {bf:9.country} omitted because of collinearity.{p_end}
{p 0 6 2}note: {bf:11.country} omitted because of collinearity.{p_end}
{p 0 6 2}note: {bf:12.country} omitted because of collinearity.{p_end}

Linear regression                               Number of obs     = {res}    22,541
                                                {txt}F(48, 22492)      =  {res}    65.62
                                                {txt}Prob > F          = {res}    0.0000
                                                {txt}R-squared         = {res}    0.1121
                                                {txt}Root MSE          =    {res} .25446

{txt}{hline 24}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 25}{c |}{col 37}    Robust
{col 1}              eval_sant{col 25}{c |} Coefficient{col 37}  std. err.{col 49}      t{col 57}   P>|t|{col 65}     [95% con{col 78}f. interval]
{hline 24}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 3}bad_health_situation {c |}{col 25}{res}{space 2}-.0052059{col 37}{space 2} .0048155{col 48}{space 1}   -1.08{col 57}{space 3}0.280{col 65}{space 4}-.0146447{col 78}{space 3} .0042329
{txt}{space 7}blame_gov_health {c |}{col 25}{res}{space 2}-.0178774{col 37}{space 2} .0048166{col 48}{space 1}   -3.71{col 57}{space 3}0.000{col 65}{space 4}-.0273183{col 78}{space 3}-.0084365
{txt}{space 4}praising_gov_health {c |}{col 25}{res}{space 2} .0126024{col 37}{space 2} .0047788{col 48}{space 1}    2.64{col 57}{space 3}0.008{col 65}{space 4} .0032357{col 78}{space 3} .0219691
{txt}{space 5}bad_econ_situation {c |}{col 25}{res}{space 2}-.0013669{col 37}{space 2} .0047915{col 48}{space 1}   -0.29{col 57}{space 3}0.775{col 65}{space 4}-.0107587{col 78}{space 3} .0080248
{txt}{space 9}blame_gov_econ {c |}{col 25}{res}{space 2} .0001903{col 37}{space 2} .0048051{col 48}{space 1}    0.04{col 57}{space 3}0.968{col 65}{space 4} -.009228{col 78}{space 3} .0096085
{txt}{space 6}praising_gov_econ {c |}{col 25}{res}{space 2} .0040853{col 37}{space 2} .0047882{col 48}{space 1}    0.85{col 57}{space 3}0.394{col 65}{space 4}-.0052999{col 78}{space 3} .0134706
{txt}{space 15}thirties {c |}{col 25}{res}{space 2}-.0190246{col 37}{space 2}  .005817{col 48}{space 1}   -3.27{col 57}{space 3}0.001{col 65}{space 4}-.0304263{col 78}{space 3}-.0076228
{txt}{space 15}fourties {c |}{col 25}{res}{space 2} -.027223{col 37}{space 2} .0058648{col 48}{space 1}   -4.64{col 57}{space 3}0.000{col 65}{space 4}-.0387184{col 78}{space 3}-.0157276
{txt}{space 16}fifties {c |}{col 25}{res}{space 2}-.0228076{col 37}{space 2} .0061341{col 48}{space 1}   -3.72{col 57}{space 3}0.000{col 65}{space 4}-.0348309{col 78}{space 3}-.0107843
{txt}{space 16}sixties {c |}{col 25}{res}{space 2}-.0155191{col 37}{space 2} .0061073{col 48}{space 1}   -2.54{col 57}{space 3}0.011{col 65}{space 4}-.0274898{col 78}{space 3}-.0035484
{txt}{space 14}seventies {c |}{col 25}{res}{space 2}-.0059295{col 37}{space 2} .0073791{col 48}{space 1}   -0.80{col 57}{space 3}0.422{col 65}{space 4}-.0203931{col 78}{space 3}  .008534
{txt}{space 8}income2quartile {c |}{col 25}{res}{space 2} .0189359{col 37}{space 2} .0050463{col 48}{space 1}    3.75{col 57}{space 3}0.000{col 65}{space 4} .0090448{col 78}{space 3}  .028827
{txt}{space 8}income3quartile {c |}{col 25}{res}{space 2} .0173656{col 37}{space 2} .0051665{col 48}{space 1}    3.36{col 57}{space 3}0.001{col 65}{space 4} .0072388{col 78}{space 3} .0274924
{txt}{space 8}income4quartile {c |}{col 25}{res}{space 2}  .037782{col 37}{space 2} .0053324{col 48}{space 1}    7.09{col 57}{space 3}0.000{col 65}{space 4} .0273301{col 78}{space 3} .0482338
{txt}{space 9}incomenoanswer {c |}{col 25}{res}{space 2}-.0106879{col 37}{space 2} .0081894{col 48}{space 1}   -1.31{col 57}{space 3}0.192{col 65}{space 4}-.0267396{col 78}{space 3} .0053638
{txt}{space 17}female {c |}{col 25}{res}{space 2} .0016287{col 37}{space 2} .0034844{col 48}{space 1}    0.47{col 57}{space 3}0.640{col 65}{space 4}-.0052009{col 78}{space 3} .0084583
{txt}{space 13}highschool {c |}{col 25}{res}{space 2} .0077207{col 37}{space 2}  .005739{col 48}{space 1}    1.35{col 57}{space 3}0.179{col 65}{space 4}-.0035282{col 78}{space 3} .0189695
{txt}{space 16}college {c |}{col 25}{res}{space 2} .0066927{col 37}{space 2} .0053716{col 48}{space 1}    1.25{col 57}{space 3}0.213{col 65}{space 4} -.003836{col 78}{space 3} .0172214
{txt}{space 13}noreligion {c |}{col 25}{res}{space 2}-.0138483{col 37}{space 2} .0074088{col 48}{space 1}   -1.87{col 57}{space 3}0.062{col 65}{space 4}-.0283701{col 78}{space 3} .0006735
{txt}{space 3}christiannotcatholic {c |}{col 25}{res}{space 2} .0500826{col 37}{space 2} .0085094{col 48}{space 1}    5.89{col 57}{space 3}0.000{col 65}{space 4} .0334036{col 78}{space 3} .0667617
{txt}{space 15}catholic {c |}{col 25}{res}{space 2} .0247563{col 37}{space 2} .0075925{col 48}{space 1}    3.26{col 57}{space 3}0.001{col 65}{space 4} .0098744{col 78}{space 3} .0396381
{txt}{space 9}fulltimeworker {c |}{col 25}{res}{space 2}-.2202164{col 37}{space 2}  .008279{col 48}{space 1}  -26.60{col 57}{space 3}0.000{col 65}{space 4}-.2364438{col 78}{space 3}-.2039891
{txt}{space 9}parttimeworker {c |}{col 25}{res}{space 2} -.212119{col 37}{space 2} .0228861{col 48}{space 1}   -9.27{col 57}{space 3}0.000{col 65}{space 4}-.2569774{col 78}{space 3}-.1672606
{txt}{space 13}unemployed {c |}{col 25}{res}{space 2} -.242406{col 37}{space 2} .0137369{col 48}{space 1}  -17.65{col 57}{space 3}0.000{col 65}{space 4}-.2693313{col 78}{space 3}-.2154808
{txt}{space 11}selfemployed {c |}{col 25}{res}{space 2}-.2435322{col 37}{space 2} .0339641{col 48}{space 1}   -7.17{col 57}{space 3}0.000{col 65}{space 4}-.3101041{col 78}{space 3}-.1769602
{txt}{space 16}outofLF {c |}{col 25}{res}{space 2}-.2274191{col 37}{space 2}  .009006{col 48}{space 1}  -25.25{col 57}{space 3}0.000{col 65}{space 4}-.2450715{col 78}{space 3}-.2097666
{txt}{space 13}goodhealth {c |}{col 25}{res}{space 2} .0500205{col 37}{space 2} .0036506{col 48}{space 1}   13.70{col 57}{space 3}0.000{col 65}{space 4} .0428652{col 78}{space 3} .0571759
{txt}{space 18}white {c |}{col 25}{res}{space 2} .0323921{col 37}{space 2} .0348058{col 48}{space 1}    0.93{col 57}{space 3}0.352{col 65}{space 4}-.0358297{col 78}{space 3} .1006139
{txt}{space 18}black {c |}{col 25}{res}{space 2}  .053737{col 37}{space 2} .0397313{col 48}{space 1}    1.35{col 57}{space 3}0.176{col 65}{space 4} -.024139{col 78}{space 3} .1316131
{txt}{space 17}latino {c |}{col 25}{res}{space 2} .0631639{col 37}{space 2} .0438141{col 48}{space 1}    1.44{col 57}{space 3}0.149{col 65}{space 4}-.0227148{col 78}{space 3} .1490425
{txt}{space 18}asian {c |}{col 25}{res}{space 2} .0397889{col 37}{space 2} .0451501{col 48}{space 1}    0.88{col 57}{space 3}0.378{col 65}{space 4}-.0487084{col 78}{space 3} .1282862
{txt}{space 12}whitecollar {c |}{col 25}{res}{space 2}-.0121464{col 37}{space 2}  .008923{col 48}{space 1}   -1.36{col 57}{space 3}0.173{col 65}{space 4} -.029636{col 78}{space 3} .0053432
{txt}{space 13}bluecollar {c |}{col 25}{res}{space 2}-.0265615{col 37}{space 2} .0091179{col 48}{space 1}   -2.91{col 57}{space 3}0.004{col 65}{space 4}-.0444332{col 78}{space 3}-.0086899
{txt}{space 10}serviceworker {c |}{col 25}{res}{space 2}-.0117713{col 37}{space 2} .0072274{col 48}{space 1}   -1.63{col 57}{space 3}0.103{col 65}{space 4}-.0259376{col 78}{space 3}  .002395
{txt}{space 12}mv_thirties {c |}{col 25}{res}{space 2}        0{col 37}{txt}  (omitted)
{space 12}mv_fourties {c |}{col 25}{res}{space 2}        0{col 37}{txt}  (omitted)
{space 13}mv_fifties {c |}{col 25}{res}{space 2}        0{col 37}{txt}  (omitted)
{space 13}mv_sixties {c |}{col 25}{res}{space 2}        0{col 37}{txt}  (omitted)
{space 11}mv_seventies {c |}{col 25}{res}{space 2}        0{col 37}{txt}  (omitted)
{space 5}mv_income2quartile {c |}{col 25}{res}{space 2}        0{col 37}{txt}  (omitted)
{space 5}mv_income3quartile {c |}{col 25}{res}{space 2}        0{col 37}{txt}  (omitted)
{space 5}mv_income4quartile {c |}{col 25}{res}{space 2}        0{col 37}{txt}  (omitted)
{space 6}mv_incomenoanswer {c |}{col 25}{res}{space 2}-.0385358{col 37}{space 2} .0104191{col 48}{space 1}   -3.70{col 57}{space 3}0.000{col 65}{space 4}-.0589579{col 78}{space 3}-.0181137
{txt}{space 14}mv_female {c |}{col 25}{res}{space 2}        0{col 37}{txt}  (omitted)
{space 10}mv_highschool {c |}{col 25}{res}{space 2}-.0017489{col 37}{space 2} .0139649{col 48}{space 1}   -0.13{col 57}{space 3}0.900{col 65}{space 4} -.029121{col 78}{space 3} .0256232
{txt}{space 13}mv_college {c |}{col 25}{res}{space 2}        0{col 37}{txt}  (omitted)
{space 10}mv_noreligion {c |}{col 25}{res}{space 2} .0047669{col 37}{space 2} .0274849{col 48}{space 1}    0.17{col 57}{space 3}0.862{col 65}{space 4}-.0491055{col 78}{space 3} .0586393
{txt}mv_christiannotcatholic {c |}{col 25}{res}{space 2}        0{col 37}{txt}  (omitted)
{space 12}mv_catholic {c |}{col 25}{res}{space 2}        0{col 37}{txt}  (omitted)
{space 6}mv_fulltimeworker {c |}{col 25}{res}{space 2}        0{col 37}{txt}  (omitted)
{space 6}mv_parttimeworker {c |}{col 25}{res}{space 2}-.1991596{col 37}{space 2}  .016826{col 48}{space 1}  -11.84{col 57}{space 3}0.000{col 65}{space 4}-.2321397{col 78}{space 3}-.1661794
{txt}{space 10}mv_unemployed {c |}{col 25}{res}{space 2}        0{col 37}{txt}  (omitted)
{space 8}mv_selfemployed {c |}{col 25}{res}{space 2} .2251016{col 37}{space 2} .0184589{col 48}{space 1}   12.19{col 57}{space 3}0.000{col 65}{space 4} .1889209{col 78}{space 3} .2612823
{txt}{space 13}mv_outofLF {c |}{col 25}{res}{space 2}        0{col 37}{txt}  (omitted)
{space 10}mv_goodhealth {c |}{col 25}{res}{space 2} .2009462{col 37}{space 2} .0920227{col 48}{space 1}    2.18{col 57}{space 3}0.029{col 65}{space 4} .0205754{col 78}{space 3}  .381317
{txt}{space 15}mv_white {c |}{col 25}{res}{space 2} .2974142{col 37}{space 2}   .03458{col 48}{space 1}    8.60{col 57}{space 3}0.000{col 65}{space 4} .2296351{col 78}{space 3} .3651934
{txt}{space 15}mv_black {c |}{col 25}{res}{space 2}        0{col 37}{txt}  (omitted)
{space 14}mv_latino {c |}{col 25}{res}{space 2}        0{col 37}{txt}  (omitted)
{space 15}mv_asian {c |}{col 25}{res}{space 2}        0{col 37}{txt}  (omitted)
{space 9}mv_whitecollar {c |}{col 25}{res}{space 2} .2465079{col 37}{space 2} .0178114{col 48}{space 1}   13.84{col 57}{space 3}0.000{col 65}{space 4} .2115962{col 78}{space 3} .2814195
{txt}{space 10}mv_bluecollar {c |}{col 25}{res}{space 2}        0{col 37}{txt}  (omitted)
{space 7}mv_serviceworker {c |}{col 25}{res}{space 2}        0{col 37}{txt}  (omitted)
{space 23} {c |}
{space 16}country {c |}
{space 15}Austria  {c |}{col 25}{res}{space 2}  .462613{col 37}{space 2} .0388764{col 48}{space 1}   11.90{col 57}{space 3}0.000{col 65}{space 4} .3864125{col 78}{space 3} .5388135
{txt}{space 16}Brazil  {c |}{col 25}{res}{space 2}-.0220255{col 37}{space 2} .0184088{col 48}{space 1}   -1.20{col 57}{space 3}0.232{col 65}{space 4} -.058108{col 78}{space 3} .0140569
{txt}{space 16}France  {c |}{col 25}{res}{space 2}        0{col 37}{txt}  (omitted)
{space 15}Germany  {c |}{col 25}{res}{space 2}  .471624{col 37}{space 2}  .038413{col 48}{space 1}   12.28{col 57}{space 3}0.000{col 65}{space 4} .3963318{col 78}{space 3} .5469162
{txt}{space 17}Italy  {c |}{col 25}{res}{space 2} .4221836{col 37}{space 2} .0470336{col 48}{space 1}    8.98{col 57}{space 3}0.000{col 65}{space 4} .3299944{col 78}{space 3} .5143728
{txt}{space 11}New_Zealand  {c |}{col 25}{res}{space 2}        0{col 37}{txt}  (omitted)
{space 16}Poland  {c |}{col 25}{res}{space 2} .0119544{col 37}{space 2} .0182914{col 48}{space 1}    0.65{col 57}{space 3}0.513{col 65}{space 4}-.0238979{col 78}{space 3} .0478068
{txt}{space 17}Spain  {c |}{col 25}{res}{space 2}        0{col 37}{txt}  (omitted)
{space 16}Sweden  {c |}{col 25}{res}{space 2} .0523797{col 37}{space 2} .0178499{col 48}{space 1}    2.93{col 57}{space 3}0.003{col 65}{space 4} .0173927{col 78}{space 3} .0873667
{txt}{space 20}UK  {c |}{col 25}{res}{space 2}        0{col 37}{txt}  (omitted)
{space 20}US  {c |}{col 25}{res}{space 2}        0{col 37}{txt}  (omitted)
{space 23} {c |}
{space 18}_cons {c |}{col 25}{res}{space 2} .0875614{col 37}{space 2} .0406074{col 48}{space 1}    2.16{col 57}{space 3}0.031{col 65}{space 4} .0079682{col 78}{space 3} .1671547
{txt}{hline 24}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{res}{txt}({res}est2{txt} stored)

{com}. estadd local Controls "X", replace

{txt}added macro:
           e(Controls) : "{res:X}"

{com}. estadd local Country_FE "X", replace

{txt}added macro:
         e(Country_FE) : "{res:X}"

{com}. mylincom3

{p 0 7}{space 1}{text:( 1)}{space 1} {res}blame_gov_health - praising_gov_health = 0{p_end}

{txt}{hline 13}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 1}   eval_sant{col 14}{c |} Coefficient{col 26}  Std. err.{col 38}      t{col 46}   P>|t|{col 54}     [95% con{col 67}f. interval]
{hline 13}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 9}(1) {c |}{col 14}{res}{space 2}-.0304798{col 26}{space 2} .0067853{col 37}{space 1}   -4.49{col 46}{space 3}0.000{col 54}{space 4}-.0437794{col 67}{space 3}-.0171802
{txt}{hline 13}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}

added macro:
             e(blameh) : "{res:-0.030***}"

added scalar:
           e(blamehsd) =  {res}.00678527

{p 0 7}{space 1}{text:( 1)}{space 1} blame_gov_econ - praising_gov_econ = 0{p_end}

{txt}{hline 13}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 1}   eval_sant{col 14}{c |} Coefficient{col 26}  Std. err.{col 38}      t{col 46}   P>|t|{col 54}     [95% con{col 67}f. interval]
{hline 13}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 9}(1) {c |}{col 14}{res}{space 2}-.0038951{col 26}{space 2} .0067836{col 37}{space 1}   -0.57{col 46}{space 3}0.566{col 54}{space 4}-.0171914{col 67}{space 3} .0094012
{txt}{hline 13}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}

added macro:
             e(blamee) : "{res:-0.004}"

added scalar:
           e(seblamee) =  {res}.00678359
{txt}
{com}. get_mean

{txt}added scalar:
                 e(av) =  {res}.509
{txt}
{com}. 
. eststo: reg eval_eco bad_health_situation blame_gov_health praising_gov_health bad_econ_situation blame_gov_econ praising_gov_econ, robust

{txt}Linear regression                               Number of obs     = {res}    22,541
                                                {txt}F(6, 22534)       =  {res}     4.72
                                                {txt}Prob > F          = {res}    0.0001
                                                {txt}R-squared         = {res}    0.0013
                                                {txt}Root MSE          =    {res} .25831

{txt}{hline 21}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 22}{c |}{col 34}    Robust
{col 1}            eval_eco{col 22}{c |} Coefficient{col 34}  std. err.{col 46}      t{col 54}   P>|t|{col 62}     [95% con{col 75}f. interval]
{hline 21}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
bad_health_situation {c |}{col 22}{res}{space 2} .0031297{col 34}{space 2} .0048993{col 45}{space 1}    0.64{col 54}{space 3}0.523{col 62}{space 4}-.0064733{col 75}{space 3} .0127327
{txt}{space 4}blame_gov_health {c |}{col 22}{res}{space 2}-.0068015{col 34}{space 2} .0048896{col 45}{space 1}   -1.39{col 54}{space 3}0.164{col 62}{space 4}-.0163854{col 75}{space 3} .0027824
{txt}{space 1}praising_gov_health {c |}{col 22}{res}{space 2} .0063652{col 34}{space 2} .0048442{col 45}{space 1}    1.31{col 54}{space 3}0.189{col 62}{space 4}-.0031298{col 75}{space 3} .0158603
{txt}{space 2}bad_econ_situation {c |}{col 22}{res}{space 2}-.0077619{col 34}{space 2} .0048778{col 45}{space 1}   -1.59{col 54}{space 3}0.112{col 62}{space 4}-.0173228{col 75}{space 3}  .001799
{txt}{space 6}blame_gov_econ {c |}{col 22}{res}{space 2}-.0099366{col 34}{space 2} .0048689{col 45}{space 1}   -2.04{col 54}{space 3}0.041{col 62}{space 4}-.0194799{col 75}{space 3}-.0003932
{txt}{space 3}praising_gov_econ {c |}{col 22}{res}{space 2} .0042309{col 34}{space 2} .0048638{col 45}{space 1}    0.87{col 54}{space 3}0.384{col 62}{space 4}-.0053026{col 75}{space 3} .0137643
{txt}{space 15}_cons {c |}{col 22}{res}{space 2}  .505528{col 34}{space 2} .0046003{col 45}{space 1}  109.89{col 54}{space 3}0.000{col 62}{space 4} .4965112{col 75}{space 3} .5145448
{txt}{hline 21}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{res}{txt}({res}est3{txt} stored)

{com}. mylincom3

{p 0 7}{space 1}{text:( 1)}{space 1} {res}blame_gov_health - praising_gov_health = 0{p_end}

{txt}{hline 13}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 1}    eval_eco{col 14}{c |} Coefficient{col 26}  Std. err.{col 38}      t{col 46}   P>|t|{col 54}     [95% con{col 67}f. interval]
{hline 13}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 9}(1) {c |}{col 14}{res}{space 2}-.0131668{col 26}{space 2} .0068828{col 37}{space 1}   -1.91{col 46}{space 3}0.056{col 54}{space 4}-.0266574{col 67}{space 3} .0003239
{txt}{hline 13}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}

added macro:
             e(blameh) : "{res:-0.013*}"

added scalar:
           e(blamehsd) =  {res}.00688276

{p 0 7}{space 1}{text:( 1)}{space 1} blame_gov_econ - praising_gov_econ = 0{p_end}

{txt}{hline 13}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 1}    eval_eco{col 14}{c |} Coefficient{col 26}  Std. err.{col 38}      t{col 46}   P>|t|{col 54}     [95% con{col 67}f. interval]
{hline 13}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 9}(1) {c |}{col 14}{res}{space 2}-.0141674{col 26}{space 2} .0068818{col 37}{space 1}   -2.06{col 46}{space 3}0.040{col 54}{space 4}-.0276563{col 67}{space 3}-.0006786
{txt}{hline 13}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}

added macro:
             e(blamee) : "{res:-0.014**}"

added scalar:
           e(seblamee) =  {res}.00688182
{txt}
{com}. get_mean

{txt}added scalar:
                 e(av) =  {res}.502
{txt}
{com}. 
. eststo: reg eval_eco bad_health_situation blame_gov_health praising_gov_health bad_econ_situation blame_gov_econ praising_gov_econ $controls $mv_controls  i.country, robust
{txt}{p 0 6 2}note: {bf:mv_thirties} omitted because of collinearity.{p_end}
{p 0 6 2}note: {bf:mv_fourties} omitted because of collinearity.{p_end}
{p 0 6 2}note: {bf:mv_fifties} omitted because of collinearity.{p_end}
{p 0 6 2}note: {bf:mv_sixties} omitted because of collinearity.{p_end}
{p 0 6 2}note: {bf:mv_seventies} omitted because of collinearity.{p_end}
{p 0 6 2}note: {bf:mv_income2quartile} omitted because of collinearity.{p_end}
{p 0 6 2}note: {bf:mv_income3quartile} omitted because of collinearity.{p_end}
{p 0 6 2}note: {bf:mv_income4quartile} omitted because of collinearity.{p_end}
{p 0 6 2}note: {bf:mv_female} omitted because of collinearity.{p_end}
{p 0 6 2}note: {bf:mv_college} omitted because of collinearity.{p_end}
{p 0 6 2}note: {bf:mv_christiannotcatholic} omitted because of collinearity.{p_end}
{p 0 6 2}note: {bf:mv_catholic} omitted because of collinearity.{p_end}
{p 0 6 2}note: {bf:mv_fulltimeworker} omitted because of collinearity.{p_end}
{p 0 6 2}note: {bf:mv_unemployed} omitted because of collinearity.{p_end}
{p 0 6 2}note: {bf:mv_outofLF} omitted because of collinearity.{p_end}
{p 0 6 2}note: {bf:mv_black} omitted because of collinearity.{p_end}
{p 0 6 2}note: {bf:mv_latino} omitted because of collinearity.{p_end}
{p 0 6 2}note: {bf:mv_asian} omitted because of collinearity.{p_end}
{p 0 6 2}note: {bf:mv_bluecollar} omitted because of collinearity.{p_end}
{p 0 6 2}note: {bf:mv_serviceworker} omitted because of collinearity.{p_end}
{p 0 6 2}note: {bf:4.country} omitted because of collinearity.{p_end}
{p 0 6 2}note: {bf:7.country} omitted because of collinearity.{p_end}
{p 0 6 2}note: {bf:9.country} omitted because of collinearity.{p_end}
{p 0 6 2}note: {bf:11.country} omitted because of collinearity.{p_end}
{p 0 6 2}note: {bf:12.country} omitted because of collinearity.{p_end}

Linear regression                               Number of obs     = {res}    22,541
                                                {txt}F(48, 22492)      =  {res}    50.54
                                                {txt}Prob > F          = {res}    0.0000
                                                {txt}R-squared         = {res}    0.0955
                                                {txt}Root MSE          =    {res} .24604

{txt}{hline 24}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 25}{c |}{col 37}    Robust
{col 1}               eval_eco{col 25}{c |} Coefficient{col 37}  std. err.{col 49}      t{col 57}   P>|t|{col 65}     [95% con{col 78}f. interval]
{hline 24}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 3}bad_health_situation {c |}{col 25}{res}{space 2} .0035858{col 37}{space 2} .0046767{col 48}{space 1}    0.77{col 57}{space 3}0.443{col 65}{space 4}-.0055808{col 78}{space 3} .0127524
{txt}{space 7}blame_gov_health {c |}{col 25}{res}{space 2}-.0075481{col 37}{space 2} .0046519{col 48}{space 1}   -1.62{col 57}{space 3}0.105{col 65}{space 4}-.0166662{col 78}{space 3}   .00157
{txt}{space 4}praising_gov_health {c |}{col 25}{res}{space 2} .0072019{col 37}{space 2} .0046267{col 48}{space 1}    1.56{col 57}{space 3}0.120{col 65}{space 4}-.0018667{col 78}{space 3} .0162706
{txt}{space 5}bad_econ_situation {c |}{col 25}{res}{space 2}-.0076786{col 37}{space 2} .0046484{col 48}{space 1}   -1.65{col 57}{space 3}0.099{col 65}{space 4}-.0167898{col 78}{space 3} .0014326
{txt}{space 9}blame_gov_econ {c |}{col 25}{res}{space 2}-.0099316{col 37}{space 2} .0046249{col 48}{space 1}   -2.15{col 57}{space 3}0.032{col 65}{space 4}-.0189968{col 78}{space 3}-.0008664
{txt}{space 6}praising_gov_econ {c |}{col 25}{res}{space 2} .0044199{col 37}{space 2} .0046515{col 48}{space 1}    0.95{col 57}{space 3}0.342{col 65}{space 4}-.0046973{col 78}{space 3} .0135372
{txt}{space 15}thirties {c |}{col 25}{res}{space 2}-.0114306{col 37}{space 2} .0057217{col 48}{space 1}   -2.00{col 57}{space 3}0.046{col 65}{space 4}-.0226456{col 78}{space 3}-.0002156
{txt}{space 15}fourties {c |}{col 25}{res}{space 2} -.018627{col 37}{space 2} .0057075{col 48}{space 1}   -3.26{col 57}{space 3}0.001{col 65}{space 4}-.0298141{col 78}{space 3}  -.00744
{txt}{space 16}fifties {c |}{col 25}{res}{space 2}-.0134146{col 37}{space 2}  .005974{col 48}{space 1}   -2.25{col 57}{space 3}0.025{col 65}{space 4} -.025124{col 78}{space 3}-.0017051
{txt}{space 16}sixties {c |}{col 25}{res}{space 2} .0000401{col 37}{space 2} .0059199{col 48}{space 1}    0.01{col 57}{space 3}0.995{col 65}{space 4}-.0115632{col 78}{space 3} .0116435
{txt}{space 14}seventies {c |}{col 25}{res}{space 2} .0198346{col 37}{space 2} .0070709{col 48}{space 1}    2.81{col 57}{space 3}0.005{col 65}{space 4} .0059751{col 78}{space 3}  .033694
{txt}{space 8}income2quartile {c |}{col 25}{res}{space 2} .0218474{col 37}{space 2} .0048803{col 48}{space 1}    4.48{col 57}{space 3}0.000{col 65}{space 4} .0122817{col 78}{space 3} .0314132
{txt}{space 8}income3quartile {c |}{col 25}{res}{space 2} .0237974{col 37}{space 2} .0050096{col 48}{space 1}    4.75{col 57}{space 3}0.000{col 65}{space 4} .0139783{col 78}{space 3} .0336165
{txt}{space 8}income4quartile {c |}{col 25}{res}{space 2} .0437171{col 37}{space 2} .0051803{col 48}{space 1}    8.44{col 57}{space 3}0.000{col 65}{space 4} .0335634{col 78}{space 3} .0538708
{txt}{space 9}incomenoanswer {c |}{col 25}{res}{space 2} -.009797{col 37}{space 2} .0078356{col 48}{space 1}   -1.25{col 57}{space 3}0.211{col 65}{space 4}-.0251552{col 78}{space 3} .0055613
{txt}{space 17}female {c |}{col 25}{res}{space 2}-.0008152{col 37}{space 2} .0033741{col 48}{space 1}   -0.24{col 57}{space 3}0.809{col 65}{space 4}-.0074287{col 78}{space 3} .0057982
{txt}{space 13}highschool {c |}{col 25}{res}{space 2} .0016421{col 37}{space 2} .0055833{col 48}{space 1}    0.29{col 57}{space 3}0.769{col 65}{space 4}-.0093015{col 78}{space 3} .0125857
{txt}{space 16}college {c |}{col 25}{res}{space 2}  .001262{col 37}{space 2} .0052014{col 48}{space 1}    0.24{col 57}{space 3}0.808{col 65}{space 4}-.0089331{col 78}{space 3} .0114572
{txt}{space 13}noreligion {c |}{col 25}{res}{space 2}-.0174995{col 37}{space 2} .0071885{col 48}{space 1}   -2.43{col 57}{space 3}0.015{col 65}{space 4}-.0315894{col 78}{space 3}-.0034097
{txt}{space 3}christiannotcatholic {c |}{col 25}{res}{space 2} .0390289{col 37}{space 2} .0082917{col 48}{space 1}    4.71{col 57}{space 3}0.000{col 65}{space 4} .0227766{col 78}{space 3} .0552813
{txt}{space 15}catholic {c |}{col 25}{res}{space 2} .0170587{col 37}{space 2} .0073665{col 48}{space 1}    2.32{col 57}{space 3}0.021{col 65}{space 4} .0026199{col 78}{space 3} .0314975
{txt}{space 9}fulltimeworker {c |}{col 25}{res}{space 2}-.1283424{col 37}{space 2} .0086971{col 48}{space 1}  -14.76{col 57}{space 3}0.000{col 65}{space 4}-.1453893{col 78}{space 3}-.1112955
{txt}{space 9}parttimeworker {c |}{col 25}{res}{space 2} -.123912{col 37}{space 2} .0210724{col 48}{space 1}   -5.88{col 57}{space 3}0.000{col 65}{space 4}-.1652153{col 78}{space 3}-.0826087
{txt}{space 13}unemployed {c |}{col 25}{res}{space 2}-.1595689{col 37}{space 2} .0134427{col 48}{space 1}  -11.87{col 57}{space 3}0.000{col 65}{space 4}-.1859174{col 78}{space 3}-.1332203
{txt}{space 11}selfemployed {c |}{col 25}{res}{space 2}-.1450203{col 37}{space 2} .0377282{col 48}{space 1}   -3.84{col 57}{space 3}0.000{col 65}{space 4}-.2189701{col 78}{space 3}-.0710705
{txt}{space 16}outofLF {c |}{col 25}{res}{space 2}-.1375282{col 37}{space 2} .0092914{col 48}{space 1}  -14.80{col 57}{space 3}0.000{col 65}{space 4}-.1557399{col 78}{space 3}-.1193165
{txt}{space 13}goodhealth {c |}{col 25}{res}{space 2} .0394354{col 37}{space 2}  .003539{col 48}{space 1}   11.14{col 57}{space 3}0.000{col 65}{space 4} .0324986{col 78}{space 3} .0463721
{txt}{space 18}white {c |}{col 25}{res}{space 2} .0396644{col 37}{space 2} .0356735{col 48}{space 1}    1.11{col 57}{space 3}0.266{col 65}{space 4}-.0302582{col 78}{space 3}  .109587
{txt}{space 18}black {c |}{col 25}{res}{space 2} .0293403{col 37}{space 2} .0406047{col 48}{space 1}    0.72{col 57}{space 3}0.470{col 65}{space 4}-.0502477{col 78}{space 3} .1089283
{txt}{space 17}latino {c |}{col 25}{res}{space 2}  .030209{col 37}{space 2} .0444257{col 48}{space 1}    0.68{col 57}{space 3}0.497{col 65}{space 4}-.0568683{col 78}{space 3} .1172864
{txt}{space 18}asian {c |}{col 25}{res}{space 2} .0511714{col 37}{space 2}  .044613{col 48}{space 1}    1.15{col 57}{space 3}0.251{col 65}{space 4}-.0362732{col 78}{space 3}  .138616
{txt}{space 12}whitecollar {c |}{col 25}{res}{space 2}-.0127592{col 37}{space 2} .0086594{col 48}{space 1}   -1.47{col 57}{space 3}0.141{col 65}{space 4}-.0297322{col 78}{space 3} .0042139
{txt}{space 13}bluecollar {c |}{col 25}{res}{space 2}-.0218705{col 37}{space 2} .0090016{col 48}{space 1}   -2.43{col 57}{space 3}0.015{col 65}{space 4}-.0395143{col 78}{space 3}-.0042266
{txt}{space 10}serviceworker {c |}{col 25}{res}{space 2}-.0134478{col 37}{space 2} .0071785{col 48}{space 1}   -1.87{col 57}{space 3}0.061{col 65}{space 4}-.0275182{col 78}{space 3} .0006225
{txt}{space 12}mv_thirties {c |}{col 25}{res}{space 2}        0{col 37}{txt}  (omitted)
{space 12}mv_fourties {c |}{col 25}{res}{space 2}        0{col 37}{txt}  (omitted)
{space 13}mv_fifties {c |}{col 25}{res}{space 2}        0{col 37}{txt}  (omitted)
{space 13}mv_sixties {c |}{col 25}{res}{space 2}        0{col 37}{txt}  (omitted)
{space 11}mv_seventies {c |}{col 25}{res}{space 2}        0{col 37}{txt}  (omitted)
{space 5}mv_income2quartile {c |}{col 25}{res}{space 2}        0{col 37}{txt}  (omitted)
{space 5}mv_income3quartile {c |}{col 25}{res}{space 2}        0{col 37}{txt}  (omitted)
{space 5}mv_income4quartile {c |}{col 25}{res}{space 2}        0{col 37}{txt}  (omitted)
{space 6}mv_incomenoanswer {c |}{col 25}{res}{space 2} .0246061{col 37}{space 2} .0107792{col 48}{space 1}    2.28{col 57}{space 3}0.022{col 65}{space 4} .0034781{col 78}{space 3} .0457341
{txt}{space 14}mv_female {c |}{col 25}{res}{space 2}        0{col 37}{txt}  (omitted)
{space 10}mv_highschool {c |}{col 25}{res}{space 2}-.0053501{col 37}{space 2} .0136825{col 48}{space 1}   -0.39{col 57}{space 3}0.696{col 65}{space 4}-.0321689{col 78}{space 3} .0214686
{txt}{space 13}mv_college {c |}{col 25}{res}{space 2}        0{col 37}{txt}  (omitted)
{space 10}mv_noreligion {c |}{col 25}{res}{space 2} .0396655{col 37}{space 2} .0269208{col 48}{space 1}    1.47{col 57}{space 3}0.141{col 65}{space 4}-.0131012{col 78}{space 3} .0924322
{txt}mv_christiannotcatholic {c |}{col 25}{res}{space 2}        0{col 37}{txt}  (omitted)
{space 12}mv_catholic {c |}{col 25}{res}{space 2}        0{col 37}{txt}  (omitted)
{space 6}mv_fulltimeworker {c |}{col 25}{res}{space 2}        0{col 37}{txt}  (omitted)
{space 6}mv_parttimeworker {c |}{col 25}{res}{space 2}-.1096296{col 37}{space 2} .0165101{col 48}{space 1}   -6.64{col 57}{space 3}0.000{col 65}{space 4}-.1419906{col 78}{space 3}-.0772686
{txt}{space 10}mv_unemployed {c |}{col 25}{res}{space 2}        0{col 37}{txt}  (omitted)
{space 8}mv_selfemployed {c |}{col 25}{res}{space 2} .1907007{col 37}{space 2} .0180245{col 48}{space 1}   10.58{col 57}{space 3}0.000{col 65}{space 4} .1553714{col 78}{space 3} .2260299
{txt}{space 13}mv_outofLF {c |}{col 25}{res}{space 2}        0{col 37}{txt}  (omitted)
{space 10}mv_goodhealth {c |}{col 25}{res}{space 2} .1220569{col 37}{space 2} .0995791{col 48}{space 1}    1.23{col 57}{space 3}0.220{col 65}{space 4} -.073125{col 78}{space 3} .3172389
{txt}{space 15}mv_white {c |}{col 25}{res}{space 2} .2609717{col 37}{space 2} .0355767{col 48}{space 1}    7.34{col 57}{space 3}0.000{col 65}{space 4}  .191239{col 78}{space 3} .3307044
{txt}{space 15}mv_black {c |}{col 25}{res}{space 2}        0{col 37}{txt}  (omitted)
{space 14}mv_latino {c |}{col 25}{res}{space 2}        0{col 37}{txt}  (omitted)
{space 15}mv_asian {c |}{col 25}{res}{space 2}        0{col 37}{txt}  (omitted)
{space 9}mv_whitecollar {c |}{col 25}{res}{space 2} .0941778{col 37}{space 2} .0174971{col 48}{space 1}    5.38{col 57}{space 3}0.000{col 65}{space 4} .0598824{col 78}{space 3} .1284733
{txt}{space 10}mv_bluecollar {c |}{col 25}{res}{space 2}        0{col 37}{txt}  (omitted)
{space 7}mv_serviceworker {c |}{col 25}{res}{space 2}        0{col 37}{txt}  (omitted)
{space 23} {c |}
{space 16}country {c |}
{space 15}Austria  {c |}{col 25}{res}{space 2}  .363854{col 37}{space 2} .0394991{col 48}{space 1}    9.21{col 57}{space 3}0.000{col 65}{space 4}  .286433{col 78}{space 3} .4412749
{txt}{space 16}Brazil  {c |}{col 25}{res}{space 2}-.0625031{col 37}{space 2} .0178912{col 48}{space 1}   -3.49{col 57}{space 3}0.000{col 65}{space 4}-.0975711{col 78}{space 3}-.0274351
{txt}{space 16}France  {c |}{col 25}{res}{space 2}        0{col 37}{txt}  (omitted)
{space 15}Germany  {c |}{col 25}{res}{space 2} .3652378{col 37}{space 2} .0390959{col 48}{space 1}    9.34{col 57}{space 3}0.000{col 65}{space 4}  .288607{col 78}{space 3} .4418685
{txt}{space 17}Italy  {c |}{col 25}{res}{space 2} .1450057{col 37}{space 2} .0473724{col 48}{space 1}    3.06{col 57}{space 3}0.002{col 65}{space 4} .0521525{col 78}{space 3}  .237859
{txt}{space 11}New_Zealand  {c |}{col 25}{res}{space 2}        0{col 37}{txt}  (omitted)
{space 16}Poland  {c |}{col 25}{res}{space 2}-.0763037{col 37}{space 2} .0178599{col 48}{space 1}   -4.27{col 57}{space 3}0.000{col 65}{space 4}-.1113103{col 78}{space 3} -.041297
{txt}{space 17}Spain  {c |}{col 25}{res}{space 2}        0{col 37}{txt}  (omitted)
{space 16}Sweden  {c |}{col 25}{res}{space 2}-.0093079{col 37}{space 2} .0168936{col 48}{space 1}   -0.55{col 57}{space 3}0.582{col 65}{space 4}-.0424205{col 78}{space 3} .0238048
{txt}{space 20}UK  {c |}{col 25}{res}{space 2}        0{col 37}{txt}  (omitted)
{space 20}US  {c |}{col 25}{res}{space 2}        0{col 37}{txt}  (omitted)
{space 23} {c |}
{space 18}_cons {c |}{col 25}{res}{space 2} .1401705{col 37}{space 2} .0409365{col 48}{space 1}    3.42{col 57}{space 3}0.001{col 65}{space 4} .0599321{col 78}{space 3} .2204089
{txt}{hline 24}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{res}{txt}({res}est4{txt} stored)

{com}. estadd local Controls "X", replace

{txt}added macro:
           e(Controls) : "{res:X}"

{com}. estadd local Country_FE "X", replace

{txt}added macro:
         e(Country_FE) : "{res:X}"

{com}. mylincom3

{p 0 7}{space 1}{text:( 1)}{space 1} {res}blame_gov_health - praising_gov_health = 0{p_end}

{txt}{hline 13}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 1}    eval_eco{col 14}{c |} Coefficient{col 26}  Std. err.{col 38}      t{col 46}   P>|t|{col 54}     [95% con{col 67}f. interval]
{hline 13}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 9}(1) {c |}{col 14}{res}{space 2}  -.01475{col 26}{space 2} .0065618{col 37}{space 1}   -2.25{col 46}{space 3}0.025{col 54}{space 4}-.0276116{col 67}{space 3}-.0018885
{txt}{hline 13}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}

added macro:
             e(blameh) : "{res:-0.015**}"

added scalar:
           e(blamehsd) =  {res}.00656177

{p 0 7}{space 1}{text:( 1)}{space 1} blame_gov_econ - praising_gov_econ = 0{p_end}

{txt}{hline 13}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 1}    eval_eco{col 14}{c |} Coefficient{col 26}  Std. err.{col 38}      t{col 46}   P>|t|{col 54}     [95% con{col 67}f. interval]
{hline 13}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 9}(1) {c |}{col 14}{res}{space 2}-.0143515{col 26}{space 2} .0065579{col 37}{space 1}   -2.19{col 46}{space 3}0.029{col 54}{space 4}-.0272054{col 67}{space 3}-.0014977
{txt}{hline 13}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}

added macro:
             e(blamee) : "{res:-0.014**}"

added scalar:
           e(seblamee) =  {res}.00655785
{txt}
{com}. get_mean

{txt}added scalar:
                 e(av) =  {res}.502
{txt}
{com}. 
. esttab using "3_output/2_OA/Tables/TableE4.tex", depvar cons ///
>         keep(bad_health_situation blame_gov_health praising_gov_health bad_econ_situation blame_gov_econ praising_gov_econ ) ///
>         order(bad_health_situation blame_gov_health praising_gov_health bad_econ_situation blame_gov_econ praising_gov_econ ) ///
>         label replace   cells(b(star fmt(%15.3fc)) se(par fmt(%15.3fc))) interaction(" $/times$ ") ///
>         star(* 0.10 ** 0.05 *** 0.01) ///
>         stats(Controls Country_FE N r2 av nothing nothing2 blameh blamehsd blamee seblamee, fmt(%15.0fc %15.0fc %15.0fc %9.3f %15.3fc %15.3fc %15.3fc %15.3fc %15.3fc) layout(@ @ @ @ @ @ @ @ (@) @ (@)) ///
>         labels("Individual controls" "Country FE" Observations R2 "Outcome mean" " " "Linear combination of estimates:" "Blaming - Praising health policy" " " "Blaming - Praising economic policy" " ")) ///
>         collabels(none) mlabels(none) ///
>         mgroups("Health satisfaction" "Economic satisfaction", pattern(1 0 1 0 ) prefix(\multicolumn{c -(}@span{c )-}{c -(}c{c )-}{c -(}) suffix({c )-}) span erepeat(\cmidrule(lr){c -(}@span{c )-}))
{res}{txt}(output written to {browse  `"3_output/2_OA/Tables/TableE4.tex"'})

{com}.         
.         
.         
. **# Table E.5. Satisfaction with the health and economic measures, first stage.
. eststo clear
{txt}
{com}. eststo: reg eval_sant TH1_TE1 TH1_TE2 TH1_TE3 TH1_TE4 TH2_TE1 TH2_TE2 TH2_TE3 TH2_TE4 TH3_TE1 TH3_TE2 TH3_TE3 TH3_TE4 TH4_TE1 TH4_TE2 TH4_TE3, robust

{txt}Linear regression                               Number of obs     = {res}    22,541
                                                {txt}F(15, 22525)      =  {res}     4.33
                                                {txt}Prob > F          = {res}    0.0000
                                                {txt}R-squared         = {res}    0.0029
                                                {txt}Root MSE          =    {res} .26946

{txt}{hline 13}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 14}{c |}{col 26}    Robust
{col 1}   eval_sant{col 14}{c |} Coefficient{col 26}  std. err.{col 38}      t{col 46}   P>|t|{col 54}     [95% con{col 67}f. interval]
{hline 13}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 5}TH1_TE1 {c |}{col 14}{res}{space 2}  .025586{col 26}{space 2} .0102405{col 37}{space 1}    2.50{col 46}{space 3}0.012{col 54}{space 4} .0055139{col 67}{space 3} .0456582
{txt}{space 5}TH1_TE2 {c |}{col 14}{res}{space 2} .0248166{col 26}{space 2} .0101555{col 37}{space 1}    2.44{col 46}{space 3}0.015{col 54}{space 4} .0049112{col 67}{space 3} .0447221
{txt}{space 5}TH1_TE3 {c |}{col 14}{res}{space 2} .0356178{col 26}{space 2} .0102379{col 37}{space 1}    3.48{col 46}{space 3}0.001{col 54}{space 4} .0155508{col 67}{space 3} .0556847
{txt}{space 5}TH1_TE4 {c |}{col 14}{res}{space 2} .0383382{col 26}{space 2} .0102668{col 37}{space 1}    3.73{col 46}{space 3}0.000{col 54}{space 4} .0182146{col 67}{space 3} .0584618
{txt}{space 5}TH2_TE1 {c |}{col 14}{res}{space 2} .0368812{col 26}{space 2}  .010157{col 37}{space 1}    3.63{col 46}{space 3}0.000{col 54}{space 4} .0169727{col 67}{space 3} .0567896
{txt}{space 5}TH2_TE2 {c |}{col 14}{res}{space 2} .0622696{col 26}{space 2} .0100819{col 37}{space 1}    6.18{col 46}{space 3}0.000{col 54}{space 4} .0425084{col 67}{space 3} .0820308
{txt}{space 5}TH2_TE3 {c |}{col 14}{res}{space 2} .0335783{col 26}{space 2} .0102135{col 37}{space 1}    3.29{col 46}{space 3}0.001{col 54}{space 4}  .013559{col 67}{space 3} .0535976
{txt}{space 5}TH2_TE4 {c |}{col 14}{res}{space 2} .0383733{col 26}{space 2} .0101357{col 37}{space 1}    3.79{col 46}{space 3}0.000{col 54}{space 4} .0185066{col 67}{space 3}   .05824
{txt}{space 5}TH3_TE1 {c |}{col 14}{res}{space 2} .0257407{col 26}{space 2} .0102722{col 37}{space 1}    2.51{col 46}{space 3}0.012{col 54}{space 4} .0056065{col 67}{space 3}  .045875
{txt}{space 5}TH3_TE2 {c |}{col 14}{res}{space 2} .0293687{col 26}{space 2} .0102719{col 37}{space 1}    2.86{col 46}{space 3}0.004{col 54}{space 4}  .009235{col 67}{space 3} .0495024
{txt}{space 5}TH3_TE3 {c |}{col 14}{res}{space 2} .0206018{col 26}{space 2} .0103487{col 37}{space 1}    1.99{col 46}{space 3}0.047{col 54}{space 4} .0003176{col 67}{space 3}  .040886
{txt}{space 5}TH3_TE4 {c |}{col 14}{res}{space 2} .0243548{col 26}{space 2} .0101679{col 37}{space 1}    2.40{col 46}{space 3}0.017{col 54}{space 4} .0044249{col 67}{space 3} .0442846
{txt}{space 5}TH4_TE1 {c |}{col 14}{res}{space 2} .0176646{col 26}{space 2} .0101556{col 37}{space 1}    1.74{col 46}{space 3}0.082{col 54}{space 4}-.0022411{col 67}{space 3} .0375702
{txt}{space 5}TH4_TE2 {c |}{col 14}{res}{space 2} .0056835{col 26}{space 2} .0103507{col 37}{space 1}    0.55{col 46}{space 3}0.583{col 54}{space 4}-.0146046{col 67}{space 3} .0259717
{txt}{space 5}TH4_TE3 {c |}{col 14}{res}{space 2}  .010814{col 26}{space 2} .0102567{col 37}{space 1}    1.05{col 46}{space 3}0.292{col 54}{space 4}-.0092898{col 67}{space 3} .0309178
{txt}{space 7}_cons {c |}{col 14}{res}{space 2} .4819788{col 26}{space 2} .0072818{col 37}{space 1}   66.19{col 46}{space 3}0.000{col 54}{space 4} .4677061{col 67}{space 3} .4962515
{txt}{hline 13}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{res}{txt}({res}est1{txt} stored)

{com}. estadd scalar F = e(F), replace

{txt}added scalar:
                  e(F) =  {res}4.3288565
{txt}
{com}. 
. eststo: reg eval_sant TH1_TE1 TH1_TE2 TH1_TE3 TH1_TE4 TH2_TE1 TH2_TE2 TH2_TE3 TH2_TE4 TH3_TE1 TH3_TE2 TH3_TE3 TH3_TE4 TH4_TE1 TH4_TE2 TH4_TE3 $controls $mv_controls i.country, robust
{txt}{p 0 6 2}note: {bf:mv_thirties} omitted because of collinearity.{p_end}
{p 0 6 2}note: {bf:mv_fourties} omitted because of collinearity.{p_end}
{p 0 6 2}note: {bf:mv_fifties} omitted because of collinearity.{p_end}
{p 0 6 2}note: {bf:mv_sixties} omitted because of collinearity.{p_end}
{p 0 6 2}note: {bf:mv_seventies} omitted because of collinearity.{p_end}
{p 0 6 2}note: {bf:mv_income2quartile} omitted because of collinearity.{p_end}
{p 0 6 2}note: {bf:mv_income3quartile} omitted because of collinearity.{p_end}
{p 0 6 2}note: {bf:mv_income4quartile} omitted because of collinearity.{p_end}
{p 0 6 2}note: {bf:mv_female} omitted because of collinearity.{p_end}
{p 0 6 2}note: {bf:mv_college} omitted because of collinearity.{p_end}
{p 0 6 2}note: {bf:mv_christiannotcatholic} omitted because of collinearity.{p_end}
{p 0 6 2}note: {bf:mv_catholic} omitted because of collinearity.{p_end}
{p 0 6 2}note: {bf:mv_fulltimeworker} omitted because of collinearity.{p_end}
{p 0 6 2}note: {bf:mv_unemployed} omitted because of collinearity.{p_end}
{p 0 6 2}note: {bf:mv_outofLF} omitted because of collinearity.{p_end}
{p 0 6 2}note: {bf:mv_black} omitted because of collinearity.{p_end}
{p 0 6 2}note: {bf:mv_latino} omitted because of collinearity.{p_end}
{p 0 6 2}note: {bf:mv_asian} omitted because of collinearity.{p_end}
{p 0 6 2}note: {bf:mv_bluecollar} omitted because of collinearity.{p_end}
{p 0 6 2}note: {bf:mv_serviceworker} omitted because of collinearity.{p_end}
{p 0 6 2}note: {bf:4.country} omitted because of collinearity.{p_end}
{p 0 6 2}note: {bf:7.country} omitted because of collinearity.{p_end}
{p 0 6 2}note: {bf:9.country} omitted because of collinearity.{p_end}
{p 0 6 2}note: {bf:11.country} omitted because of collinearity.{p_end}
{p 0 6 2}note: {bf:12.country} omitted because of collinearity.{p_end}

Linear regression                               Number of obs     = {res}    22,541
                                                {txt}F(57, 22483)      =  {res}    55.67
                                                {txt}Prob > F          = {res}    0.0000
                                                {txt}R-squared         = {res}    0.1127
                                                {txt}Root MSE          =    {res} .25443

{txt}{hline 24}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 25}{c |}{col 37}    Robust
{col 1}              eval_sant{col 25}{c |} Coefficient{col 37}  std. err.{col 49}      t{col 57}   P>|t|{col 65}     [95% con{col 78}f. interval]
{hline 24}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 16}TH1_TE1 {c |}{col 25}{res}{space 2} .0268033{col 37}{space 2} .0096915{col 48}{space 1}    2.77{col 57}{space 3}0.006{col 65}{space 4} .0078072{col 78}{space 3} .0457994
{txt}{space 16}TH1_TE2 {c |}{col 25}{res}{space 2} .0256592{col 37}{space 2} .0096065{col 48}{space 1}    2.67{col 57}{space 3}0.008{col 65}{space 4} .0068298{col 78}{space 3} .0444885
{txt}{space 16}TH1_TE3 {c |}{col 25}{res}{space 2} .0359528{col 37}{space 2} .0096371{col 48}{space 1}    3.73{col 57}{space 3}0.000{col 65}{space 4} .0170633{col 78}{space 3} .0548422
{txt}{space 16}TH1_TE4 {c |}{col 25}{res}{space 2} .0382754{col 37}{space 2} .0097699{col 48}{space 1}    3.92{col 57}{space 3}0.000{col 65}{space 4} .0191256{col 78}{space 3} .0574252
{txt}{space 16}TH2_TE1 {c |}{col 25}{res}{space 2} .0383862{col 37}{space 2} .0095983{col 48}{space 1}    4.00{col 57}{space 3}0.000{col 65}{space 4} .0195729{col 78}{space 3} .0571995
{txt}{space 16}TH2_TE2 {c |}{col 25}{res}{space 2} .0622486{col 37}{space 2} .0095166{col 48}{space 1}    6.54{col 57}{space 3}0.000{col 65}{space 4} .0435954{col 78}{space 3} .0809018
{txt}{space 16}TH2_TE3 {c |}{col 25}{res}{space 2}  .035921{col 37}{space 2} .0096057{col 48}{space 1}    3.74{col 57}{space 3}0.000{col 65}{space 4} .0170931{col 78}{space 3} .0547489
{txt}{space 16}TH2_TE4 {c |}{col 25}{res}{space 2} .0405497{col 37}{space 2} .0096555{col 48}{space 1}    4.20{col 57}{space 3}0.000{col 65}{space 4} .0216242{col 78}{space 3} .0594752
{txt}{space 16}TH3_TE1 {c |}{col 25}{res}{space 2} .0271944{col 37}{space 2} .0096671{col 48}{space 1}    2.81{col 57}{space 3}0.005{col 65}{space 4} .0082462{col 78}{space 3} .0461425
{txt}{space 16}TH3_TE2 {c |}{col 25}{res}{space 2} .0321949{col 37}{space 2} .0097301{col 48}{space 1}    3.31{col 57}{space 3}0.001{col 65}{space 4} .0131232{col 78}{space 3} .0512667
{txt}{space 16}TH3_TE3 {c |}{col 25}{res}{space 2} .0203658{col 37}{space 2} .0096905{col 48}{space 1}    2.10{col 57}{space 3}0.036{col 65}{space 4} .0013719{col 78}{space 3} .0393598
{txt}{space 16}TH3_TE4 {c |}{col 25}{res}{space 2} .0260208{col 37}{space 2} .0096002{col 48}{space 1}    2.71{col 57}{space 3}0.007{col 65}{space 4} .0072038{col 78}{space 3} .0448379
{txt}{space 16}TH4_TE1 {c |}{col 25}{res}{space 2} .0170673{col 37}{space 2} .0096641{col 48}{space 1}    1.77{col 57}{space 3}0.077{col 65}{space 4}-.0018749{col 78}{space 3} .0360096
{txt}{space 16}TH4_TE2 {c |}{col 25}{res}{space 2} .0054066{col 37}{space 2} .0096937{col 48}{space 1}    0.56{col 57}{space 3}0.577{col 65}{space 4}-.0135937{col 78}{space 3} .0244069
{txt}{space 16}TH4_TE3 {c |}{col 25}{res}{space 2} .0116756{col 37}{space 2} .0096354{col 48}{space 1}    1.21{col 57}{space 3}0.226{col 65}{space 4}-.0072105{col 78}{space 3} .0305618
{txt}{space 15}thirties {c |}{col 25}{res}{space 2}-.0189976{col 37}{space 2} .0058195{col 48}{space 1}   -3.26{col 57}{space 3}0.001{col 65}{space 4}-.0304043{col 78}{space 3}-.0075909
{txt}{space 15}fourties {c |}{col 25}{res}{space 2}-.0270138{col 37}{space 2} .0058674{col 48}{space 1}   -4.60{col 57}{space 3}0.000{col 65}{space 4}-.0385144{col 78}{space 3}-.0155133
{txt}{space 16}fifties {c |}{col 25}{res}{space 2}-.0225995{col 37}{space 2} .0061335{col 48}{space 1}   -3.68{col 57}{space 3}0.000{col 65}{space 4}-.0346216{col 78}{space 3}-.0105774
{txt}{space 16}sixties {c |}{col 25}{res}{space 2}-.0152578{col 37}{space 2} .0061093{col 48}{space 1}   -2.50{col 57}{space 3}0.013{col 65}{space 4}-.0272324{col 78}{space 3}-.0032833
{txt}{space 14}seventies {c |}{col 25}{res}{space 2}-.0056569{col 37}{space 2} .0073817{col 48}{space 1}   -0.77{col 57}{space 3}0.443{col 65}{space 4}-.0201255{col 78}{space 3} .0088116
{txt}{space 8}income2quartile {c |}{col 25}{res}{space 2} .0187986{col 37}{space 2}  .005049{col 48}{space 1}    3.72{col 57}{space 3}0.000{col 65}{space 4} .0089023{col 78}{space 3} .0286949
{txt}{space 8}income3quartile {c |}{col 25}{res}{space 2} .0175451{col 37}{space 2} .0051678{col 48}{space 1}    3.40{col 57}{space 3}0.001{col 65}{space 4}  .007416{col 78}{space 3} .0276743
{txt}{space 8}income4quartile {c |}{col 25}{res}{space 2}  .038046{col 37}{space 2} .0053345{col 48}{space 1}    7.13{col 57}{space 3}0.000{col 65}{space 4}   .02759{col 78}{space 3}  .048502
{txt}{space 9}incomenoanswer {c |}{col 25}{res}{space 2}-.0104603{col 37}{space 2} .0081905{col 48}{space 1}   -1.28{col 57}{space 3}0.202{col 65}{space 4}-.0265143{col 78}{space 3} .0055937
{txt}{space 17}female {c |}{col 25}{res}{space 2} .0017433{col 37}{space 2} .0034851{col 48}{space 1}    0.50{col 57}{space 3}0.617{col 65}{space 4}-.0050877{col 78}{space 3} .0085742
{txt}{space 13}highschool {c |}{col 25}{res}{space 2} .0078406{col 37}{space 2} .0057412{col 48}{space 1}    1.37{col 57}{space 3}0.172{col 65}{space 4}-.0034125{col 78}{space 3} .0190937
{txt}{space 16}college {c |}{col 25}{res}{space 2} .0064613{col 37}{space 2} .0053761{col 48}{space 1}    1.20{col 57}{space 3}0.229{col 65}{space 4}-.0040763{col 78}{space 3} .0169989
{txt}{space 13}noreligion {c |}{col 25}{res}{space 2}-.0136087{col 37}{space 2} .0074069{col 48}{space 1}   -1.84{col 57}{space 3}0.066{col 65}{space 4}-.0281268{col 78}{space 3} .0009094
{txt}{space 3}christiannotcatholic {c |}{col 25}{res}{space 2} .0502442{col 37}{space 2} .0085031{col 48}{space 1}    5.91{col 57}{space 3}0.000{col 65}{space 4} .0335775{col 78}{space 3} .0669109
{txt}{space 15}catholic {c |}{col 25}{res}{space 2}  .024918{col 37}{space 2} .0075902{col 48}{space 1}    3.28{col 57}{space 3}0.001{col 65}{space 4} .0100406{col 78}{space 3} .0397955
{txt}{space 9}fulltimeworker {c |}{col 25}{res}{space 2}-.2202392{col 37}{space 2}  .008277{col 48}{space 1}  -26.61{col 57}{space 3}0.000{col 65}{space 4}-.2364627{col 78}{space 3}-.2040156
{txt}{space 9}parttimeworker {c |}{col 25}{res}{space 2}-.2114555{col 37}{space 2} .0229502{col 48}{space 1}   -9.21{col 57}{space 3}0.000{col 65}{space 4}-.2564395{col 78}{space 3}-.1664716
{txt}{space 13}unemployed {c |}{col 25}{res}{space 2}-.2425939{col 37}{space 2} .0137532{col 48}{space 1}  -17.64{col 57}{space 3}0.000{col 65}{space 4}-.2695511{col 78}{space 3}-.2156367
{txt}{space 11}selfemployed {c |}{col 25}{res}{space 2} -.244222{col 37}{space 2} .0341106{col 48}{space 1}   -7.16{col 57}{space 3}0.000{col 65}{space 4}-.3110811{col 78}{space 3}-.1773629
{txt}{space 16}outofLF {c |}{col 25}{res}{space 2}-.2274611{col 37}{space 2} .0090022{col 48}{space 1}  -25.27{col 57}{space 3}0.000{col 65}{space 4} -.245106{col 78}{space 3}-.2098163
{txt}{space 13}goodhealth {c |}{col 25}{res}{space 2} .0500191{col 37}{space 2} .0036501{col 48}{space 1}   13.70{col 57}{space 3}0.000{col 65}{space 4} .0428647{col 78}{space 3} .0571734
{txt}{space 18}white {c |}{col 25}{res}{space 2} .0309377{col 37}{space 2} .0348145{col 48}{space 1}    0.89{col 57}{space 3}0.374{col 65}{space 4}-.0373011{col 78}{space 3} .0991765
{txt}{space 18}black {c |}{col 25}{res}{space 2}  .052404{col 37}{space 2}  .039708{col 48}{space 1}    1.32{col 57}{space 3}0.187{col 65}{space 4}-.0254265{col 78}{space 3} .1302344
{txt}{space 17}latino {c |}{col 25}{res}{space 2} .0610869{col 37}{space 2} .0437828{col 48}{space 1}    1.40{col 57}{space 3}0.163{col 65}{space 4}-.0247305{col 78}{space 3} .1469043
{txt}{space 18}asian {c |}{col 25}{res}{space 2} .0378656{col 37}{space 2} .0451573{col 48}{space 1}    0.84{col 57}{space 3}0.402{col 65}{space 4}-.0506458{col 78}{space 3} .1263771
{txt}{space 12}whitecollar {c |}{col 25}{res}{space 2}-.0123885{col 37}{space 2} .0089244{col 48}{space 1}   -1.39{col 57}{space 3}0.165{col 65}{space 4} -.029881{col 78}{space 3}  .005104
{txt}{space 13}bluecollar {c |}{col 25}{res}{space 2}-.0266005{col 37}{space 2} .0091211{col 48}{space 1}   -2.92{col 57}{space 3}0.004{col 65}{space 4}-.0444786{col 78}{space 3}-.0087225
{txt}{space 10}serviceworker {c |}{col 25}{res}{space 2}-.0117209{col 37}{space 2} .0072271{col 48}{space 1}   -1.62{col 57}{space 3}0.105{col 65}{space 4}-.0258865{col 78}{space 3} .0024447
{txt}{space 12}mv_thirties {c |}{col 25}{res}{space 2}        0{col 37}{txt}  (omitted)
{space 12}mv_fourties {c |}{col 25}{res}{space 2}        0{col 37}{txt}  (omitted)
{space 13}mv_fifties {c |}{col 25}{res}{space 2}        0{col 37}{txt}  (omitted)
{space 13}mv_sixties {c |}{col 25}{res}{space 2}        0{col 37}{txt}  (omitted)
{space 11}mv_seventies {c |}{col 25}{res}{space 2}        0{col 37}{txt}  (omitted)
{space 5}mv_income2quartile {c |}{col 25}{res}{space 2}        0{col 37}{txt}  (omitted)
{space 5}mv_income3quartile {c |}{col 25}{res}{space 2}        0{col 37}{txt}  (omitted)
{space 5}mv_income4quartile {c |}{col 25}{res}{space 2}        0{col 37}{txt}  (omitted)
{space 6}mv_incomenoanswer {c |}{col 25}{res}{space 2}-.0386632{col 37}{space 2} .0104111{col 48}{space 1}   -3.71{col 57}{space 3}0.000{col 65}{space 4}-.0590698{col 78}{space 3}-.0182567
{txt}{space 14}mv_female {c |}{col 25}{res}{space 2}        0{col 37}{txt}  (omitted)
{space 10}mv_highschool {c |}{col 25}{res}{space 2}-.0016996{col 37}{space 2} .0139532{col 48}{space 1}   -0.12{col 57}{space 3}0.903{col 65}{space 4}-.0290488{col 78}{space 3} .0256497
{txt}{space 13}mv_college {c |}{col 25}{res}{space 2}        0{col 37}{txt}  (omitted)
{space 10}mv_noreligion {c |}{col 25}{res}{space 2} .0054025{col 37}{space 2} .0274743{col 48}{space 1}    0.20{col 57}{space 3}0.844{col 65}{space 4} -.048449{col 78}{space 3} .0592541
{txt}mv_christiannotcatholic {c |}{col 25}{res}{space 2}        0{col 37}{txt}  (omitted)
{space 12}mv_catholic {c |}{col 25}{res}{space 2}        0{col 37}{txt}  (omitted)
{space 6}mv_fulltimeworker {c |}{col 25}{res}{space 2}        0{col 37}{txt}  (omitted)
{space 6}mv_parttimeworker {c |}{col 25}{res}{space 2} -.199157{col 37}{space 2} .0168185{col 48}{space 1}  -11.84{col 57}{space 3}0.000{col 65}{space 4}-.2321224{col 78}{space 3}-.1661916
{txt}{space 10}mv_unemployed {c |}{col 25}{res}{space 2}        0{col 37}{txt}  (omitted)
{space 8}mv_selfemployed {c |}{col 25}{res}{space 2} .2250612{col 37}{space 2} .0184604{col 48}{space 1}   12.19{col 57}{space 3}0.000{col 65}{space 4} .1888775{col 78}{space 3} .2612449
{txt}{space 13}mv_outofLF {c |}{col 25}{res}{space 2}        0{col 37}{txt}  (omitted)
{space 10}mv_goodhealth {c |}{col 25}{res}{space 2} .1969939{col 37}{space 2} .0911984{col 48}{space 1}    2.16{col 57}{space 3}0.031{col 65}{space 4} .0182387{col 78}{space 3} .3757491
{txt}{space 15}mv_white {c |}{col 25}{res}{space 2} .2957798{col 37}{space 2} .0345934{col 48}{space 1}    8.55{col 57}{space 3}0.000{col 65}{space 4} .2279743{col 78}{space 3} .3635852
{txt}{space 15}mv_black {c |}{col 25}{res}{space 2}        0{col 37}{txt}  (omitted)
{space 14}mv_latino {c |}{col 25}{res}{space 2}        0{col 37}{txt}  (omitted)
{space 15}mv_asian {c |}{col 25}{res}{space 2}        0{col 37}{txt}  (omitted)
{space 9}mv_whitecollar {c |}{col 25}{res}{space 2} .2466003{col 37}{space 2} .0177956{col 48}{space 1}   13.86{col 57}{space 3}0.000{col 65}{space 4} .2117197{col 78}{space 3} .2814809
{txt}{space 10}mv_bluecollar {c |}{col 25}{res}{space 2}        0{col 37}{txt}  (omitted)
{space 7}mv_serviceworker {c |}{col 25}{res}{space 2}        0{col 37}{txt}  (omitted)
{space 23} {c |}
{space 16}country {c |}
{space 15}Austria  {c |}{col 25}{res}{space 2} .4610137{col 37}{space 2} .0388829{col 48}{space 1}   11.86{col 57}{space 3}0.000{col 65}{space 4} .3848005{col 78}{space 3}  .537227
{txt}{space 16}Brazil  {c |}{col 25}{res}{space 2}-.0217823{col 37}{space 2} .0183937{col 48}{space 1}   -1.18{col 57}{space 3}0.236{col 65}{space 4}-.0578353{col 78}{space 3} .0142708
{txt}{space 16}France  {c |}{col 25}{res}{space 2}        0{col 37}{txt}  (omitted)
{space 15}Germany  {c |}{col 25}{res}{space 2} .4699872{col 37}{space 2} .0384172{col 48}{space 1}   12.23{col 57}{space 3}0.000{col 65}{space 4} .3946869{col 78}{space 3} .5452876
{txt}{space 17}Italy  {c |}{col 25}{res}{space 2} .4201562{col 37}{space 2}  .047037{col 48}{space 1}    8.93{col 57}{space 3}0.000{col 65}{space 4} .3279603{col 78}{space 3} .5123521
{txt}{space 11}New_Zealand  {c |}{col 25}{res}{space 2}        0{col 37}{txt}  (omitted)
{space 16}Poland  {c |}{col 25}{res}{space 2} .0120405{col 37}{space 2} .0182752{col 48}{space 1}    0.66{col 57}{space 3}0.510{col 65}{space 4}-.0237803{col 78}{space 3} .0478612
{txt}{space 17}Spain  {c |}{col 25}{res}{space 2}        0{col 37}{txt}  (omitted)
{space 16}Sweden  {c |}{col 25}{res}{space 2} .0526012{col 37}{space 2} .0178304{col 48}{space 1}    2.95{col 57}{space 3}0.003{col 65}{space 4} .0176523{col 78}{space 3}   .08755
{txt}{space 20}UK  {c |}{col 25}{res}{space 2}        0{col 37}{txt}  (omitted)
{space 20}US  {c |}{col 25}{res}{space 2}        0{col 37}{txt}  (omitted)
{space 23} {c |}
{space 18}_cons {c |}{col 25}{res}{space 2} .0574787{col 37}{space 2} .0407901{col 48}{space 1}    1.41{col 57}{space 3}0.159{col 65}{space 4}-.0224727{col 78}{space 3} .1374302
{txt}{hline 24}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{res}{txt}({res}est2{txt} stored)

{com}. estadd local CFE "X", replace

{txt}added macro:
                e(CFE) : "{res:X}"

{com}. estadd local Controls "X", replace

{txt}added macro:
           e(Controls) : "{res:X}"

{com}. test  TH1_TE1 TH1_TE2 TH1_TE3 TH1_TE4 TH2_TE1 TH2_TE2 TH2_TE3 TH2_TE4 TH3_TE1 TH3_TE2 TH3_TE3 TH3_TE4 TH4_TE1 TH4_TE2 TH4_TE3

{p 0 7}{space 1}{text:( 1)}{space 1} {res}TH1_TE1 = 0{p_end}
{p 0 7}{space 1}{text:( 2)}{space 1} TH1_TE2 = 0{p_end}
{p 0 7}{space 1}{text:( 3)}{space 1} TH1_TE3 = 0{p_end}
{p 0 7}{space 1}{text:( 4)}{space 1} TH1_TE4 = 0{p_end}
{p 0 7}{space 1}{text:( 5)}{space 1} TH2_TE1 = 0{p_end}
{p 0 7}{space 1}{text:( 6)}{space 1} TH2_TE2 = 0{p_end}
{p 0 7}{space 1}{text:( 7)}{space 1} TH2_TE3 = 0{p_end}
{p 0 7}{space 1}{text:( 8)}{space 1} TH2_TE4 = 0{p_end}
{p 0 7}{space 1}{text:( 9)}{space 1} TH3_TE1 = 0{p_end}
{p 0 7}{space 1}{text:(10)}{space 1} TH3_TE2 = 0{p_end}
{p 0 7}{space 1}{text:(11)}{space 1} TH3_TE3 = 0{p_end}
{p 0 7}{space 1}{text:(12)}{space 1} TH3_TE4 = 0{p_end}
{p 0 7}{space 1}{text:(13)}{space 1} TH4_TE1 = 0{p_end}
{p 0 7}{space 1}{text:(14)}{space 1} TH4_TE2 = 0{p_end}
{p 0 7}{space 1}{text:(15)}{space 1} TH4_TE3 = 0{p_end}

{txt}       F( 15, 22483) ={res}    5.05
{txt}{col 13}Prob > F ={res}    0.0000
{txt}
{com}. estadd scalar F = r(F), replace

{txt}added scalar:
                  e(F) =  {res}5.0456036
{txt}
{com}. 
. eststo: reg eval_sant healthc econc, robust

{txt}Linear regression                               Number of obs     = {res}    22,541
                                                {txt}F(2, 22538)       =  {res}    22.24
                                                {txt}Prob > F          = {res}    0.0000
                                                {txt}R-squared         = {res}    0.0020
                                                {txt}Root MSE          =    {res}  .2695

{txt}{hline 13}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 14}{c |}{col 26}    Robust
{col 1}   eval_sant{col 14}{c |} Coefficient{col 26}  std. err.{col 38}      t{col 46}   P>|t|{col 54}     [95% con{col 67}f. interval]
{hline 13}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 5}healthc {c |}{col 14}{res}{space 2}  .029799{col 26}{space 2}  .004531{col 37}{space 1}    6.58{col 46}{space 3}0.000{col 54}{space 4}  .020918{col 67}{space 3} .0386801
{txt}{space 7}econc {c |}{col 14}{res}{space 2} .0048471{col 26}{space 2} .0045365{col 37}{space 1}    1.07{col 46}{space 3}0.285{col 54}{space 4}-.0040447{col 67}{space 3} .0137388
{txt}{space 7}_cons {c |}{col 14}{res}{space 2} .4915248{col 26}{space 2} .0036882{col 37}{space 1}  133.27{col 46}{space 3}0.000{col 54}{space 4} .4842957{col 67}{space 3} .4987538
{txt}{hline 13}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{res}{txt}({res}est3{txt} stored)

{com}. estadd scalar F = e(F), replace

{txt}added scalar:
                  e(F) =  {res}22.241508
{txt}
{com}.         
. eststo: reg eval_sant healthc econc $controls $mv_controls i.country, robust
{txt}{p 0 6 2}note: {bf:mv_thirties} omitted because of collinearity.{p_end}
{p 0 6 2}note: {bf:mv_fourties} omitted because of collinearity.{p_end}
{p 0 6 2}note: {bf:mv_fifties} omitted because of collinearity.{p_end}
{p 0 6 2}note: {bf:mv_sixties} omitted because of collinearity.{p_end}
{p 0 6 2}note: {bf:mv_seventies} omitted because of collinearity.{p_end}
{p 0 6 2}note: {bf:mv_income2quartile} omitted because of collinearity.{p_end}
{p 0 6 2}note: {bf:mv_income3quartile} omitted because of collinearity.{p_end}
{p 0 6 2}note: {bf:mv_income4quartile} omitted because of collinearity.{p_end}
{p 0 6 2}note: {bf:mv_female} omitted because of collinearity.{p_end}
{p 0 6 2}note: {bf:mv_college} omitted because of collinearity.{p_end}
{p 0 6 2}note: {bf:mv_christiannotcatholic} omitted because of collinearity.{p_end}
{p 0 6 2}note: {bf:mv_catholic} omitted because of collinearity.{p_end}
{p 0 6 2}note: {bf:mv_fulltimeworker} omitted because of collinearity.{p_end}
{p 0 6 2}note: {bf:mv_unemployed} omitted because of collinearity.{p_end}
{p 0 6 2}note: {bf:mv_outofLF} omitted because of collinearity.{p_end}
{p 0 6 2}note: {bf:mv_black} omitted because of collinearity.{p_end}
{p 0 6 2}note: {bf:mv_latino} omitted because of collinearity.{p_end}
{p 0 6 2}note: {bf:mv_asian} omitted because of collinearity.{p_end}
{p 0 6 2}note: {bf:mv_bluecollar} omitted because of collinearity.{p_end}
{p 0 6 2}note: {bf:mv_serviceworker} omitted because of collinearity.{p_end}
{p 0 6 2}note: {bf:4.country} omitted because of collinearity.{p_end}
{p 0 6 2}note: {bf:7.country} omitted because of collinearity.{p_end}
{p 0 6 2}note: {bf:9.country} omitted because of collinearity.{p_end}
{p 0 6 2}note: {bf:11.country} omitted because of collinearity.{p_end}
{p 0 6 2}note: {bf:12.country} omitted because of collinearity.{p_end}

Linear regression                               Number of obs     = {res}    22,541
                                                {txt}F(44, 22496)      =  {res}    71.34
                                                {txt}Prob > F          = {res}    0.0000
                                                {txt}R-squared         = {res}    0.1118
                                                {txt}Root MSE          =    {res} .25448

{txt}{hline 24}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 25}{c |}{col 37}    Robust
{col 1}              eval_sant{col 25}{c |} Coefficient{col 37}  std. err.{col 49}      t{col 57}   P>|t|{col 65}     [95% con{col 78}f. interval]
{hline 24}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 16}healthc {c |}{col 25}{res}{space 2} .0306615{col 37}{space 2} .0042806{col 48}{space 1}    7.16{col 57}{space 3}0.000{col 65}{space 4} .0222713{col 78}{space 3} .0390517
{txt}{space 18}econc {c |}{col 25}{res}{space 2} .0047756{col 37}{space 2} .0042952{col 48}{space 1}    1.11{col 57}{space 3}0.266{col 65}{space 4}-.0036433{col 78}{space 3} .0131945
{txt}{space 15}thirties {c |}{col 25}{res}{space 2}-.0189485{col 37}{space 2} .0058171{col 48}{space 1}   -3.26{col 57}{space 3}0.001{col 65}{space 4}-.0303504{col 78}{space 3}-.0075467
{txt}{space 15}fourties {c |}{col 25}{res}{space 2}-.0271395{col 37}{space 2} .0058638{col 48}{space 1}   -4.63{col 57}{space 3}0.000{col 65}{space 4} -.038633{col 78}{space 3} -.015646
{txt}{space 16}fifties {c |}{col 25}{res}{space 2}-.0227493{col 37}{space 2} .0061348{col 48}{space 1}   -3.71{col 57}{space 3}0.000{col 65}{space 4}-.0347739{col 78}{space 3}-.0107247
{txt}{space 16}sixties {c |}{col 25}{res}{space 2}-.0154559{col 37}{space 2} .0061066{col 48}{space 1}   -2.53{col 57}{space 3}0.011{col 65}{space 4}-.0274253{col 78}{space 3}-.0034864
{txt}{space 14}seventies {c |}{col 25}{res}{space 2}-.0059337{col 37}{space 2} .0073769{col 48}{space 1}   -0.80{col 57}{space 3}0.421{col 65}{space 4}-.0203929{col 78}{space 3} .0085255
{txt}{space 8}income2quartile {c |}{col 25}{res}{space 2} .0190626{col 37}{space 2}  .005045{col 48}{space 1}    3.78{col 57}{space 3}0.000{col 65}{space 4} .0091741{col 78}{space 3} .0289511
{txt}{space 8}income3quartile {c |}{col 25}{res}{space 2} .0174218{col 37}{space 2} .0051659{col 48}{space 1}    3.37{col 57}{space 3}0.001{col 65}{space 4} .0072962{col 78}{space 3} .0275474
{txt}{space 8}income4quartile {c |}{col 25}{res}{space 2} .0378046{col 37}{space 2} .0053315{col 48}{space 1}    7.09{col 57}{space 3}0.000{col 65}{space 4} .0273544{col 78}{space 3} .0482548
{txt}{space 9}incomenoanswer {c |}{col 25}{res}{space 2}-.0107621{col 37}{space 2} .0081873{col 48}{space 1}   -1.31{col 57}{space 3}0.189{col 65}{space 4}-.0268097{col 78}{space 3} .0052856
{txt}{space 17}female {c |}{col 25}{res}{space 2} .0017173{col 37}{space 2} .0034848{col 48}{space 1}    0.49{col 57}{space 3}0.622{col 65}{space 4}-.0051132{col 78}{space 3} .0085479
{txt}{space 13}highschool {c |}{col 25}{res}{space 2} .0076714{col 37}{space 2} .0057369{col 48}{space 1}    1.34{col 57}{space 3}0.181{col 65}{space 4}-.0035735{col 78}{space 3} .0189162
{txt}{space 16}college {c |}{col 25}{res}{space 2} .0066459{col 37}{space 2} .0053705{col 48}{space 1}    1.24{col 57}{space 3}0.216{col 65}{space 4}-.0038807{col 78}{space 3} .0171725
{txt}{space 13}noreligion {c |}{col 25}{res}{space 2}-.0137327{col 37}{space 2} .0074073{col 48}{space 1}   -1.85{col 57}{space 3}0.064{col 65}{space 4}-.0282516{col 78}{space 3} .0007862
{txt}{space 3}christiannotcatholic {c |}{col 25}{res}{space 2} .0500367{col 37}{space 2} .0085086{col 48}{space 1}    5.88{col 57}{space 3}0.000{col 65}{space 4} .0333593{col 78}{space 3} .0667141
{txt}{space 15}catholic {c |}{col 25}{res}{space 2} .0249043{col 37}{space 2} .0075912{col 48}{space 1}    3.28{col 57}{space 3}0.001{col 65}{space 4}  .010025{col 78}{space 3} .0397836
{txt}{space 9}fulltimeworker {c |}{col 25}{res}{space 2}-.2203194{col 37}{space 2} .0082721{col 48}{space 1}  -26.63{col 57}{space 3}0.000{col 65}{space 4}-.2365334{col 78}{space 3}-.2041055
{txt}{space 9}parttimeworker {c |}{col 25}{res}{space 2}-.2124215{col 37}{space 2} .0228686{col 48}{space 1}   -9.29{col 57}{space 3}0.000{col 65}{space 4}-.2572455{col 78}{space 3}-.1675975
{txt}{space 13}unemployed {c |}{col 25}{res}{space 2}-.2425439{col 37}{space 2} .0137243{col 48}{space 1}  -17.67{col 57}{space 3}0.000{col 65}{space 4}-.2694444{col 78}{space 3}-.2156434
{txt}{space 11}selfemployed {c |}{col 25}{res}{space 2}-.2440245{col 37}{space 2} .0339104{col 48}{space 1}   -7.20{col 57}{space 3}0.000{col 65}{space 4}-.3104913{col 78}{space 3}-.1775577
{txt}{space 16}outofLF {c |}{col 25}{res}{space 2}-.2274146{col 37}{space 2} .0089991{col 48}{space 1}  -25.27{col 57}{space 3}0.000{col 65}{space 4}-.2450534{col 78}{space 3}-.2097758
{txt}{space 13}goodhealth {c |}{col 25}{res}{space 2} .0499616{col 37}{space 2} .0036507{col 48}{space 1}   13.69{col 57}{space 3}0.000{col 65}{space 4} .0428061{col 78}{space 3} .0571171
{txt}{space 18}white {c |}{col 25}{res}{space 2} .0315407{col 37}{space 2}  .034828{col 48}{space 1}    0.91{col 57}{space 3}0.365{col 65}{space 4}-.0367245{col 78}{space 3} .0998059
{txt}{space 18}black {c |}{col 25}{res}{space 2} .0528511{col 37}{space 2} .0397535{col 48}{space 1}    1.33{col 57}{space 3}0.184{col 65}{space 4}-.0250686{col 78}{space 3} .1307708
{txt}{space 17}latino {c |}{col 25}{res}{space 2} .0623075{col 37}{space 2} .0438194{col 48}{space 1}    1.42{col 57}{space 3}0.155{col 65}{space 4}-.0235816{col 78}{space 3} .1481965
{txt}{space 18}asian {c |}{col 25}{res}{space 2} .0393465{col 37}{space 2} .0452044{col 48}{space 1}    0.87{col 57}{space 3}0.384{col 65}{space 4}-.0492571{col 78}{space 3} .1279502
{txt}{space 12}whitecollar {c |}{col 25}{res}{space 2}-.0119833{col 37}{space 2}  .008923{col 48}{space 1}   -1.34{col 57}{space 3}0.179{col 65}{space 4}-.0294731{col 78}{space 3} .0055064
{txt}{space 13}bluecollar {c |}{col 25}{res}{space 2}-.0266948{col 37}{space 2} .0091188{col 48}{space 1}   -2.93{col 57}{space 3}0.003{col 65}{space 4}-.0445682{col 78}{space 3}-.0088214
{txt}{space 10}serviceworker {c |}{col 25}{res}{space 2}-.0116559{col 37}{space 2} .0072298{col 48}{space 1}   -1.61{col 57}{space 3}0.107{col 65}{space 4}-.0258267{col 78}{space 3}  .002515
{txt}{space 12}mv_thirties {c |}{col 25}{res}{space 2}        0{col 37}{txt}  (omitted)
{space 12}mv_fourties {c |}{col 25}{res}{space 2}        0{col 37}{txt}  (omitted)
{space 13}mv_fifties {c |}{col 25}{res}{space 2}        0{col 37}{txt}  (omitted)
{space 13}mv_sixties {c |}{col 25}{res}{space 2}        0{col 37}{txt}  (omitted)
{space 11}mv_seventies {c |}{col 25}{res}{space 2}        0{col 37}{txt}  (omitted)
{space 5}mv_income2quartile {c |}{col 25}{res}{space 2}        0{col 37}{txt}  (omitted)
{space 5}mv_income3quartile {c |}{col 25}{res}{space 2}        0{col 37}{txt}  (omitted)
{space 5}mv_income4quartile {c |}{col 25}{res}{space 2}        0{col 37}{txt}  (omitted)
{space 6}mv_incomenoanswer {c |}{col 25}{res}{space 2}-.0386266{col 37}{space 2} .0104111{col 48}{space 1}   -3.71{col 57}{space 3}0.000{col 65}{space 4}-.0590331{col 78}{space 3}-.0182201
{txt}{space 14}mv_female {c |}{col 25}{res}{space 2}        0{col 37}{txt}  (omitted)
{space 10}mv_highschool {c |}{col 25}{res}{space 2}-.0018014{col 37}{space 2} .0139634{col 48}{space 1}   -0.13{col 57}{space 3}0.897{col 65}{space 4}-.0291705{col 78}{space 3} .0255678
{txt}{space 13}mv_college {c |}{col 25}{res}{space 2}        0{col 37}{txt}  (omitted)
{space 10}mv_noreligion {c |}{col 25}{res}{space 2}  .005206{col 37}{space 2} .0275002{col 48}{space 1}    0.19{col 57}{space 3}0.850{col 65}{space 4}-.0486963{col 78}{space 3} .0591083
{txt}mv_christiannotcatholic {c |}{col 25}{res}{space 2}        0{col 37}{txt}  (omitted)
{space 12}mv_catholic {c |}{col 25}{res}{space 2}        0{col 37}{txt}  (omitted)
{space 6}mv_fulltimeworker {c |}{col 25}{res}{space 2}        0{col 37}{txt}  (omitted)
{space 6}mv_parttimeworker {c |}{col 25}{res}{space 2}-.1992678{col 37}{space 2} .0168178{col 48}{space 1}  -11.85{col 57}{space 3}0.000{col 65}{space 4}-.2322319{col 78}{space 3}-.1663038
{txt}{space 10}mv_unemployed {c |}{col 25}{res}{space 2}        0{col 37}{txt}  (omitted)
{space 8}mv_selfemployed {c |}{col 25}{res}{space 2} .2252346{col 37}{space 2} .0184488{col 48}{space 1}   12.21{col 57}{space 3}0.000{col 65}{space 4} .1890737{col 78}{space 3} .2613954
{txt}{space 13}mv_outofLF {c |}{col 25}{res}{space 2}        0{col 37}{txt}  (omitted)
{space 10}mv_goodhealth {c |}{col 25}{res}{space 2} .2018898{col 37}{space 2} .0926909{col 48}{space 1}    2.18{col 57}{space 3}0.029{col 65}{space 4} .0202092{col 78}{space 3} .3835704
{txt}{space 15}mv_white {c |}{col 25}{res}{space 2} .2964714{col 37}{space 2} .0346033{col 48}{space 1}    8.57{col 57}{space 3}0.000{col 65}{space 4} .2286465{col 78}{space 3} .3642962
{txt}{space 15}mv_black {c |}{col 25}{res}{space 2}        0{col 37}{txt}  (omitted)
{space 14}mv_latino {c |}{col 25}{res}{space 2}        0{col 37}{txt}  (omitted)
{space 15}mv_asian {c |}{col 25}{res}{space 2}        0{col 37}{txt}  (omitted)
{space 9}mv_whitecollar {c |}{col 25}{res}{space 2} .2465382{col 37}{space 2} .0178077{col 48}{space 1}   13.84{col 57}{space 3}0.000{col 65}{space 4} .2116339{col 78}{space 3} .2814426
{txt}{space 10}mv_bluecollar {c |}{col 25}{res}{space 2}        0{col 37}{txt}  (omitted)
{space 7}mv_serviceworker {c |}{col 25}{res}{space 2}        0{col 37}{txt}  (omitted)
{space 23} {c |}
{space 16}country {c |}
{space 15}Austria  {c |}{col 25}{res}{space 2} .4616008{col 37}{space 2} .0388945{col 48}{space 1}   11.87{col 57}{space 3}0.000{col 65}{space 4} .3853649{col 78}{space 3} .5378366
{txt}{space 16}Brazil  {c |}{col 25}{res}{space 2}-.0220671{col 37}{space 2}  .018406{col 48}{space 1}   -1.20{col 57}{space 3}0.231{col 65}{space 4}-.0581442{col 78}{space 3}   .01401
{txt}{space 16}France  {c |}{col 25}{res}{space 2}        0{col 37}{txt}  (omitted)
{space 15}Germany  {c |}{col 25}{res}{space 2} .4706499{col 37}{space 2} .0384339{col 48}{space 1}   12.25{col 57}{space 3}0.000{col 65}{space 4} .3953169{col 78}{space 3}  .545983
{txt}{space 17}Italy  {c |}{col 25}{res}{space 2} .4208429{col 37}{space 2} .0470643{col 48}{space 1}    8.94{col 57}{space 3}0.000{col 65}{space 4} .3285936{col 78}{space 3} .5130922
{txt}{space 11}New_Zealand  {c |}{col 25}{res}{space 2}        0{col 37}{txt}  (omitted)
{space 16}Poland  {c |}{col 25}{res}{space 2} .0118756{col 37}{space 2} .0182925{col 48}{space 1}    0.65{col 57}{space 3}0.516{col 65}{space 4} -.023979{col 78}{space 3} .0477302
{txt}{space 17}Spain  {c |}{col 25}{res}{space 2}        0{col 37}{txt}  (omitted)
{space 16}Sweden  {c |}{col 25}{res}{space 2} .0523585{col 37}{space 2} .0178455{col 48}{space 1}    2.93{col 57}{space 3}0.003{col 65}{space 4} .0173801{col 78}{space 3} .0873369
{txt}{space 20}UK  {c |}{col 25}{res}{space 2}        0{col 37}{txt}  (omitted)
{space 20}US  {c |}{col 25}{res}{space 2}        0{col 37}{txt}  (omitted)
{space 23} {c |}
{space 18}_cons {c |}{col 25}{res}{space 2} .0670887{col 37}{space 2} .0404792{col 48}{space 1}    1.66{col 57}{space 3}0.097{col 65}{space 4}-.0122533{col 78}{space 3} .1464307
{txt}{hline 24}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{res}{txt}({res}est4{txt} stored)

{com}. estadd local CFE "X", replace

{txt}added macro:
                e(CFE) : "{res:X}"

{com}. estadd local Controls "X", replace

{txt}added macro:
           e(Controls) : "{res:X}"

{com}. test  healthc econc

{p 0 7}{space 1}{text:( 1)}{space 1} {res}healthc = 0{p_end}
{p 0 7}{space 1}{text:( 2)}{space 1} econc = 0{p_end}

{txt}       F(  2, 22496) ={res}   26.36
{txt}{col 13}Prob > F ={res}    0.0000
{txt}
{com}. estadd scalar F = r(F), replace

{txt}added scalar:
                  e(F) =  {res}26.358502
{txt}
{com}. 
. eststo: reg eval_eco TH1_TE1 TH1_TE2 TH1_TE3 TH1_TE4 TH2_TE1 TH2_TE2 TH2_TE3 TH2_TE4 TH3_TE1 TH3_TE2 TH3_TE3 TH3_TE4 TH4_TE1 TH4_TE2 TH4_TE3, robust

{txt}Linear regression                               Number of obs     = {res}    22,541
                                                {txt}F(15, 22525)      =  {res}     2.56
                                                {txt}Prob > F          = {res}    0.0008
                                                {txt}R-squared         = {res}    0.0017
                                                {txt}Root MSE          =    {res} .25831

{txt}{hline 13}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 14}{c |}{col 26}    Robust
{col 1}    eval_eco{col 14}{c |} Coefficient{col 26}  std. err.{col 38}      t{col 46}   P>|t|{col 54}     [95% con{col 67}f. interval]
{hline 13}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 5}TH1_TE1 {c |}{col 14}{res}{space 2} .0167244{col 26}{space 2} .0098871{col 37}{space 1}    1.69{col 46}{space 3}0.091{col 54}{space 4}-.0026551{col 67}{space 3} .0361038
{txt}{space 5}TH1_TE2 {c |}{col 14}{res}{space 2} .0245305{col 26}{space 2} .0097425{col 37}{space 1}    2.52{col 46}{space 3}0.012{col 54}{space 4} .0054345{col 67}{space 3} .0436265
{txt}{space 5}TH1_TE3 {c |}{col 14}{res}{space 2} .0166621{col 26}{space 2} .0098683{col 37}{space 1}    1.69{col 46}{space 3}0.091{col 54}{space 4}-.0026804{col 67}{space 3} .0360046
{txt}{space 5}TH1_TE4 {c |}{col 14}{res}{space 2} .0150673{col 26}{space 2} .0098587{col 37}{space 1}    1.53{col 46}{space 3}0.126{col 54}{space 4}-.0042564{col 67}{space 3} .0343911
{txt}{space 5}TH2_TE1 {c |}{col 14}{res}{space 2} .0321662{col 26}{space 2} .0096949{col 37}{space 1}    3.32{col 46}{space 3}0.001{col 54}{space 4} .0131634{col 67}{space 3} .0511689
{txt}{space 5}TH2_TE2 {c |}{col 14}{res}{space 2} .0442738{col 26}{space 2} .0096572{col 37}{space 1}    4.58{col 46}{space 3}0.000{col 54}{space 4}  .025345{col 67}{space 3} .0632027
{txt}{space 5}TH2_TE3 {c |}{col 14}{res}{space 2} .0150079{col 26}{space 2} .0096754{col 37}{space 1}    1.55{col 46}{space 3}0.121{col 54}{space 4}-.0039566{col 67}{space 3} .0339724
{txt}{space 5}TH2_TE4 {c |}{col 14}{res}{space 2} .0070353{col 26}{space 2} .0096768{col 37}{space 1}    0.73{col 46}{space 3}0.467{col 54}{space 4}-.0119319{col 67}{space 3} .0260025
{txt}{space 5}TH3_TE1 {c |}{col 14}{res}{space 2} .0293526{col 26}{space 2} .0098775{col 37}{space 1}    2.97{col 46}{space 3}0.003{col 54}{space 4} .0099921{col 67}{space 3} .0487131
{txt}{space 5}TH3_TE2 {c |}{col 14}{res}{space 2}  .030929{col 26}{space 2} .0097385{col 37}{space 1}    3.18{col 46}{space 3}0.001{col 54}{space 4} .0118409{col 67}{space 3} .0500171
{txt}{space 5}TH3_TE3 {c |}{col 14}{res}{space 2} .0178778{col 26}{space 2} .0097925{col 37}{space 1}    1.83{col 46}{space 3}0.068{col 54}{space 4}-.0013162{col 67}{space 3} .0370718
{txt}{space 5}TH3_TE4 {c |}{col 14}{res}{space 2} .0073245{col 26}{space 2} .0097437{col 37}{space 1}    0.75{col 46}{space 3}0.452{col 54}{space 4}-.0117738{col 67}{space 3} .0264228
{txt}{space 5}TH4_TE1 {c |}{col 14}{res}{space 2} .0216589{col 26}{space 2} .0097226{col 37}{space 1}    2.23{col 46}{space 3}0.026{col 54}{space 4} .0026019{col 67}{space 3} .0407158
{txt}{space 5}TH4_TE2 {c |}{col 14}{res}{space 2} .0170111{col 26}{space 2} .0099059{col 37}{space 1}    1.72{col 46}{space 3}0.086{col 54}{space 4}-.0024052{col 67}{space 3} .0364274
{txt}{space 5}TH4_TE3 {c |}{col 14}{res}{space 2} .0193379{col 26}{space 2} .0098221{col 37}{space 1}    1.97{col 46}{space 3}0.049{col 54}{space 4} .0000859{col 67}{space 3} .0385899
{txt}{space 7}_cons {c |}{col 14}{res}{space 2} .4819788{col 26}{space 2} .0069495{col 37}{space 1}   69.35{col 46}{space 3}0.000{col 54}{space 4} .4683573{col 67}{space 3} .4956003
{txt}{hline 13}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{res}{txt}({res}est5{txt} stored)

{com}. estadd scalar F = e(F), replace

{txt}added scalar:
                  e(F) =  {res}2.558479
{txt}
{com}. 
. eststo: reg eval_eco TH1_TE1 TH1_TE2 TH1_TE3 TH1_TE4 TH2_TE1 TH2_TE2 TH2_TE3 TH2_TE4 TH3_TE1 TH3_TE2 TH3_TE3 TH3_TE4 TH4_TE1 TH4_TE2 TH4_TE3 $controls $mv_controls i.country, robust
{txt}{p 0 6 2}note: {bf:mv_thirties} omitted because of collinearity.{p_end}
{p 0 6 2}note: {bf:mv_fourties} omitted because of collinearity.{p_end}
{p 0 6 2}note: {bf:mv_fifties} omitted because of collinearity.{p_end}
{p 0 6 2}note: {bf:mv_sixties} omitted because of collinearity.{p_end}
{p 0 6 2}note: {bf:mv_seventies} omitted because of collinearity.{p_end}
{p 0 6 2}note: {bf:mv_income2quartile} omitted because of collinearity.{p_end}
{p 0 6 2}note: {bf:mv_income3quartile} omitted because of collinearity.{p_end}
{p 0 6 2}note: {bf:mv_income4quartile} omitted because of collinearity.{p_end}
{p 0 6 2}note: {bf:mv_female} omitted because of collinearity.{p_end}
{p 0 6 2}note: {bf:mv_college} omitted because of collinearity.{p_end}
{p 0 6 2}note: {bf:mv_christiannotcatholic} omitted because of collinearity.{p_end}
{p 0 6 2}note: {bf:mv_catholic} omitted because of collinearity.{p_end}
{p 0 6 2}note: {bf:mv_fulltimeworker} omitted because of collinearity.{p_end}
{p 0 6 2}note: {bf:mv_unemployed} omitted because of collinearity.{p_end}
{p 0 6 2}note: {bf:mv_outofLF} omitted because of collinearity.{p_end}
{p 0 6 2}note: {bf:mv_black} omitted because of collinearity.{p_end}
{p 0 6 2}note: {bf:mv_latino} omitted because of collinearity.{p_end}
{p 0 6 2}note: {bf:mv_asian} omitted because of collinearity.{p_end}
{p 0 6 2}note: {bf:mv_bluecollar} omitted because of collinearity.{p_end}
{p 0 6 2}note: {bf:mv_serviceworker} omitted because of collinearity.{p_end}
{p 0 6 2}note: {bf:4.country} omitted because of collinearity.{p_end}
{p 0 6 2}note: {bf:7.country} omitted because of collinearity.{p_end}
{p 0 6 2}note: {bf:9.country} omitted because of collinearity.{p_end}
{p 0 6 2}note: {bf:11.country} omitted because of collinearity.{p_end}
{p 0 6 2}note: {bf:12.country} omitted because of collinearity.{p_end}

Linear regression                               Number of obs     = {res}    22,541
                                                {txt}F(57, 22483)      =  {res}    42.96
                                                {txt}Prob > F          = {res}    0.0000
                                                {txt}R-squared         = {res}    0.0959
                                                {txt}Root MSE          =    {res} .24604

{txt}{hline 24}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 25}{c |}{col 37}    Robust
{col 1}               eval_eco{col 25}{c |} Coefficient{col 37}  std. err.{col 49}      t{col 57}   P>|t|{col 65}     [95% con{col 78}f. interval]
{hline 24}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 16}TH1_TE1 {c |}{col 25}{res}{space 2} .0175895{col 37}{space 2} .0094047{col 48}{space 1}    1.87{col 57}{space 3}0.061{col 65}{space 4}-.0008445{col 78}{space 3} .0360234
{txt}{space 16}TH1_TE2 {c |}{col 25}{res}{space 2} .0249944{col 37}{space 2} .0092454{col 48}{space 1}    2.70{col 57}{space 3}0.007{col 65}{space 4} .0068727{col 78}{space 3} .0431161
{txt}{space 16}TH1_TE3 {c |}{col 25}{res}{space 2} .0168618{col 37}{space 2} .0092875{col 48}{space 1}    1.82{col 57}{space 3}0.069{col 65}{space 4}-.0013424{col 78}{space 3}  .035066
{txt}{space 16}TH1_TE4 {c |}{col 25}{res}{space 2} .0153503{col 37}{space 2} .0093542{col 48}{space 1}    1.64{col 57}{space 3}0.101{col 65}{space 4}-.0029846{col 78}{space 3} .0336853
{txt}{space 16}TH2_TE1 {c |}{col 25}{res}{space 2}  .033479{col 37}{space 2} .0091838{col 48}{space 1}    3.65{col 57}{space 3}0.000{col 65}{space 4} .0154781{col 78}{space 3} .0514799
{txt}{space 16}TH2_TE2 {c |}{col 25}{res}{space 2} .0444505{col 37}{space 2} .0091135{col 48}{space 1}    4.88{col 57}{space 3}0.000{col 65}{space 4} .0265874{col 78}{space 3} .0623135
{txt}{space 16}TH2_TE3 {c |}{col 25}{res}{space 2} .0172541{col 37}{space 2} .0091354{col 48}{space 1}    1.89{col 57}{space 3}0.059{col 65}{space 4} -.000652{col 78}{space 3} .0351602
{txt}{space 16}TH2_TE4 {c |}{col 25}{res}{space 2} .0084416{col 37}{space 2} .0092087{col 48}{space 1}    0.92{col 57}{space 3}0.359{col 65}{space 4}-.0096081{col 78}{space 3} .0264912
{txt}{space 16}TH3_TE1 {c |}{col 25}{res}{space 2} .0305792{col 37}{space 2} .0093066{col 48}{space 1}    3.29{col 57}{space 3}0.001{col 65}{space 4} .0123377{col 78}{space 3} .0488208
{txt}{space 16}TH3_TE2 {c |}{col 25}{res}{space 2} .0331734{col 37}{space 2} .0092904{col 48}{space 1}    3.57{col 57}{space 3}0.000{col 65}{space 4} .0149635{col 78}{space 3} .0513833
{txt}{space 16}TH3_TE3 {c |}{col 25}{res}{space 2} .0169296{col 37}{space 2} .0092695{col 48}{space 1}    1.83{col 57}{space 3}0.068{col 65}{space 4}-.0012393{col 78}{space 3} .0350985
{txt}{space 16}TH3_TE4 {c |}{col 25}{res}{space 2} .0084065{col 37}{space 2} .0091853{col 48}{space 1}    0.92{col 57}{space 3}0.360{col 65}{space 4}-.0095972{col 78}{space 3} .0264103
{txt}{space 16}TH4_TE1 {c |}{col 25}{res}{space 2} .0207085{col 37}{space 2} .0092698{col 48}{space 1}    2.23{col 57}{space 3}0.025{col 65}{space 4}  .002539{col 78}{space 3} .0388779
{txt}{space 16}TH4_TE2 {c |}{col 25}{res}{space 2} .0173488{col 37}{space 2} .0093323{col 48}{space 1}    1.86{col 57}{space 3}0.063{col 65}{space 4}-.0009432{col 78}{space 3} .0356408
{txt}{space 16}TH4_TE3 {c |}{col 25}{res}{space 2} .0205653{col 37}{space 2} .0092638{col 48}{space 1}    2.22{col 57}{space 3}0.026{col 65}{space 4} .0024076{col 78}{space 3} .0387229
{txt}{space 15}thirties {c |}{col 25}{res}{space 2}-.0114105{col 37}{space 2} .0057261{col 48}{space 1}   -1.99{col 57}{space 3}0.046{col 65}{space 4}-.0226342{col 78}{space 3}-.0001869
{txt}{space 15}fourties {c |}{col 25}{res}{space 2}-.0184893{col 37}{space 2} .0057112{col 48}{space 1}   -3.24{col 57}{space 3}0.001{col 65}{space 4}-.0296837{col 78}{space 3}-.0072949
{txt}{space 16}fifties {c |}{col 25}{res}{space 2}-.0133291{col 37}{space 2}  .005978{col 48}{space 1}   -2.23{col 57}{space 3}0.026{col 65}{space 4}-.0250464{col 78}{space 3}-.0016117
{txt}{space 16}sixties {c |}{col 25}{res}{space 2} .0001642{col 37}{space 2} .0059225{col 48}{space 1}    0.03{col 57}{space 3}0.978{col 65}{space 4}-.0114442{col 78}{space 3} .0117727
{txt}{space 14}seventies {c |}{col 25}{res}{space 2} .0199082{col 37}{space 2} .0070767{col 48}{space 1}    2.81{col 57}{space 3}0.005{col 65}{space 4} .0060374{col 78}{space 3} .0337791
{txt}{space 8}income2quartile {c |}{col 25}{res}{space 2} .0217808{col 37}{space 2} .0048842{col 48}{space 1}    4.46{col 57}{space 3}0.000{col 65}{space 4} .0122074{col 78}{space 3} .0313541
{txt}{space 8}income3quartile {c |}{col 25}{res}{space 2} .0238877{col 37}{space 2} .0050106{col 48}{space 1}    4.77{col 57}{space 3}0.000{col 65}{space 4} .0140667{col 78}{space 3} .0337088
{txt}{space 8}income4quartile {c |}{col 25}{res}{space 2} .0438988{col 37}{space 2} .0051828{col 48}{space 1}    8.47{col 57}{space 3}0.000{col 65}{space 4} .0337402{col 78}{space 3} .0540574
{txt}{space 9}incomenoanswer {c |}{col 25}{res}{space 2}-.0096142{col 37}{space 2} .0078402{col 48}{space 1}   -1.23{col 57}{space 3}0.220{col 65}{space 4}-.0249816{col 78}{space 3} .0057532
{txt}{space 17}female {c |}{col 25}{res}{space 2}-.0007607{col 37}{space 2} .0033751{col 48}{space 1}   -0.23{col 57}{space 3}0.822{col 65}{space 4}-.0073762{col 78}{space 3} .0058547
{txt}{space 13}highschool {c |}{col 25}{res}{space 2} .0016587{col 37}{space 2} .0055851{col 48}{space 1}    0.30{col 57}{space 3}0.766{col 65}{space 4}-.0092886{col 78}{space 3}  .012606
{txt}{space 16}college {c |}{col 25}{res}{space 2} .0011054{col 37}{space 2} .0052054{col 48}{space 1}    0.21{col 57}{space 3}0.832{col 65}{space 4}-.0090974{col 78}{space 3} .0113083
{txt}{space 13}noreligion {c |}{col 25}{res}{space 2} -.017272{col 37}{space 2} .0071889{col 48}{space 1}   -2.40{col 57}{space 3}0.016{col 65}{space 4}-.0313627{col 78}{space 3}-.0031813
{txt}{space 3}christiannotcatholic {c |}{col 25}{res}{space 2} .0391921{col 37}{space 2} .0082887{col 48}{space 1}    4.73{col 57}{space 3}0.000{col 65}{space 4} .0229456{col 78}{space 3} .0554385
{txt}{space 15}catholic {c |}{col 25}{res}{space 2} .0173343{col 37}{space 2} .0073672{col 48}{space 1}    2.35{col 57}{space 3}0.019{col 65}{space 4}  .002894{col 78}{space 3} .0317746
{txt}{space 9}fulltimeworker {c |}{col 25}{res}{space 2}-.1284306{col 37}{space 2} .0086934{col 48}{space 1}  -14.77{col 57}{space 3}0.000{col 65}{space 4}-.1454703{col 78}{space 3} -.111391
{txt}{space 9}parttimeworker {c |}{col 25}{res}{space 2} -.123247{col 37}{space 2} .0210691{col 48}{space 1}   -5.85{col 57}{space 3}0.000{col 65}{space 4}-.1645439{col 78}{space 3}  -.08195
{txt}{space 13}unemployed {c |}{col 25}{res}{space 2}-.1598904{col 37}{space 2} .0134483{col 48}{space 1}  -11.89{col 57}{space 3}0.000{col 65}{space 4}  -.18625{col 78}{space 3}-.1335308
{txt}{space 11}selfemployed {c |}{col 25}{res}{space 2}-.1452615{col 37}{space 2}  .037736{col 48}{space 1}   -3.85{col 57}{space 3}0.000{col 65}{space 4}-.2192267{col 78}{space 3}-.0712962
{txt}{space 16}outofLF {c |}{col 25}{res}{space 2}-.1374945{col 37}{space 2} .0092856{col 48}{space 1}  -14.81{col 57}{space 3}0.000{col 65}{space 4} -.155695{col 78}{space 3} -.119294
{txt}{space 13}goodhealth {c |}{col 25}{res}{space 2} .0394619{col 37}{space 2} .0035396{col 48}{space 1}   11.15{col 57}{space 3}0.000{col 65}{space 4} .0325241{col 78}{space 3} .0463997
{txt}{space 18}white {c |}{col 25}{res}{space 2} .0387428{col 37}{space 2} .0355966{col 48}{space 1}    1.09{col 57}{space 3}0.276{col 65}{space 4}-.0310289{col 78}{space 3} .1085146
{txt}{space 18}black {c |}{col 25}{res}{space 2} .0282351{col 37}{space 2}  .040519{col 48}{space 1}    0.70{col 57}{space 3}0.486{col 65}{space 4} -.051185{col 78}{space 3} .1076552
{txt}{space 17}latino {c |}{col 25}{res}{space 2} .0287896{col 37}{space 2} .0443484{col 48}{space 1}    0.65{col 57}{space 3}0.516{col 65}{space 4}-.0581364{col 78}{space 3} .1157155
{txt}{space 18}asian {c |}{col 25}{res}{space 2} .0500028{col 37}{space 2} .0445368{col 48}{space 1}    1.12{col 57}{space 3}0.262{col 65}{space 4}-.0372923{col 78}{space 3} .1372979
{txt}{space 12}whitecollar {c |}{col 25}{res}{space 2}-.0128896{col 37}{space 2} .0086599{col 48}{space 1}   -1.49{col 57}{space 3}0.137{col 65}{space 4}-.0298636{col 78}{space 3} .0040844
{txt}{space 13}bluecollar {c |}{col 25}{res}{space 2}-.0219381{col 37}{space 2} .0090069{col 48}{space 1}   -2.44{col 57}{space 3}0.015{col 65}{space 4}-.0395923{col 78}{space 3}-.0042839
{txt}{space 10}serviceworker {c |}{col 25}{res}{space 2} -.013432{col 37}{space 2} .0071778{col 48}{space 1}   -1.87{col 57}{space 3}0.061{col 65}{space 4}-.0275011{col 78}{space 3}  .000637
{txt}{space 12}mv_thirties {c |}{col 25}{res}{space 2}        0{col 37}{txt}  (omitted)
{space 12}mv_fourties {c |}{col 25}{res}{space 2}        0{col 37}{txt}  (omitted)
{space 13}mv_fifties {c |}{col 25}{res}{space 2}        0{col 37}{txt}  (omitted)
{space 13}mv_sixties {c |}{col 25}{res}{space 2}        0{col 37}{txt}  (omitted)
{space 11}mv_seventies {c |}{col 25}{res}{space 2}        0{col 37}{txt}  (omitted)
{space 5}mv_income2quartile {c |}{col 25}{res}{space 2}        0{col 37}{txt}  (omitted)
{space 5}mv_income3quartile {c |}{col 25}{res}{space 2}        0{col 37}{txt}  (omitted)
{space 5}mv_income4quartile {c |}{col 25}{res}{space 2}        0{col 37}{txt}  (omitted)
{space 6}mv_incomenoanswer {c |}{col 25}{res}{space 2} .0244201{col 37}{space 2} .0107709{col 48}{space 1}    2.27{col 57}{space 3}0.023{col 65}{space 4} .0033084{col 78}{space 3} .0455318
{txt}{space 14}mv_female {c |}{col 25}{res}{space 2}        0{col 37}{txt}  (omitted)
{space 10}mv_highschool {c |}{col 25}{res}{space 2}-.0052083{col 37}{space 2}  .013692{col 48}{space 1}   -0.38{col 57}{space 3}0.704{col 65}{space 4}-.0320456{col 78}{space 3}  .021629
{txt}{space 13}mv_college {c |}{col 25}{res}{space 2}        0{col 37}{txt}  (omitted)
{space 10}mv_noreligion {c |}{col 25}{res}{space 2} .0400487{col 37}{space 2} .0268979{col 48}{space 1}    1.49{col 57}{space 3}0.137{col 65}{space 4}-.0126731{col 78}{space 3} .0927705
{txt}mv_christiannotcatholic {c |}{col 25}{res}{space 2}        0{col 37}{txt}  (omitted)
{space 12}mv_catholic {c |}{col 25}{res}{space 2}        0{col 37}{txt}  (omitted)
{space 6}mv_fulltimeworker {c |}{col 25}{res}{space 2}        0{col 37}{txt}  (omitted)
{space 6}mv_parttimeworker {c |}{col 25}{res}{space 2}-.1097883{col 37}{space 2} .0164972{col 48}{space 1}   -6.65{col 57}{space 3}0.000{col 65}{space 4} -.142124{col 78}{space 3}-.0774525
{txt}{space 10}mv_unemployed {c |}{col 25}{res}{space 2}        0{col 37}{txt}  (omitted)
{space 8}mv_selfemployed {c |}{col 25}{res}{space 2} .1909928{col 37}{space 2} .0180182{col 48}{space 1}   10.60{col 57}{space 3}0.000{col 65}{space 4} .1556759{col 78}{space 3} .2263098
{txt}{space 13}mv_outofLF {c |}{col 25}{res}{space 2}        0{col 37}{txt}  (omitted)
{space 10}mv_goodhealth {c |}{col 25}{res}{space 2} .1177576{col 37}{space 2} .0995949{col 48}{space 1}    1.18{col 57}{space 3}0.237{col 65}{space 4}-.0774552{col 78}{space 3} .3129705
{txt}{space 15}mv_white {c |}{col 25}{res}{space 2} .2597689{col 37}{space 2} .0355061{col 48}{space 1}    7.32{col 57}{space 3}0.000{col 65}{space 4} .1901744{col 78}{space 3} .3293634
{txt}{space 15}mv_black {c |}{col 25}{res}{space 2}        0{col 37}{txt}  (omitted)
{space 14}mv_latino {c |}{col 25}{res}{space 2}        0{col 37}{txt}  (omitted)
{space 15}mv_asian {c |}{col 25}{res}{space 2}        0{col 37}{txt}  (omitted)
{space 9}mv_whitecollar {c |}{col 25}{res}{space 2} .0943638{col 37}{space 2} .0175019{col 48}{space 1}    5.39{col 57}{space 3}0.000{col 65}{space 4} .0600589{col 78}{space 3} .1286688
{txt}{space 10}mv_bluecollar {c |}{col 25}{res}{space 2}        0{col 37}{txt}  (omitted)
{space 7}mv_serviceworker {c |}{col 25}{res}{space 2}        0{col 37}{txt}  (omitted)
{space 23} {c |}
{space 16}country {c |}
{space 15}Austria  {c |}{col 25}{res}{space 2} .3627544{col 37}{space 2} .0394402{col 48}{space 1}    9.20{col 57}{space 3}0.000{col 65}{space 4} .2854488{col 78}{space 3} .4400601
{txt}{space 16}Brazil  {c |}{col 25}{res}{space 2}-.0622506{col 37}{space 2}  .017895{col 48}{space 1}   -3.48{col 57}{space 3}0.001{col 65}{space 4} -.097326{col 78}{space 3}-.0271753
{txt}{space 16}France  {c |}{col 25}{res}{space 2}        0{col 37}{txt}  (omitted)
{space 15}Germany  {c |}{col 25}{res}{space 2} .3641837{col 37}{space 2} .0390325{col 48}{space 1}    9.33{col 57}{space 3}0.000{col 65}{space 4} .2876772{col 78}{space 3} .4406902
{txt}{space 17}Italy  {c |}{col 25}{res}{space 2} .1438143{col 37}{space 2} .0473091{col 48}{space 1}    3.04{col 57}{space 3}0.002{col 65}{space 4} .0510851{col 78}{space 3} .2365435
{txt}{space 11}New_Zealand  {c |}{col 25}{res}{space 2}        0{col 37}{txt}  (omitted)
{space 16}Poland  {c |}{col 25}{res}{space 2}-.0761657{col 37}{space 2} .0178655{col 48}{space 1}   -4.26{col 57}{space 3}0.000{col 65}{space 4}-.1111833{col 78}{space 3} -.041148
{txt}{space 17}Spain  {c |}{col 25}{res}{space 2}        0{col 37}{txt}  (omitted)
{space 16}Sweden  {c |}{col 25}{res}{space 2}-.0090201{col 37}{space 2} .0168989{col 48}{space 1}   -0.53{col 57}{space 3}0.594{col 65}{space 4}-.0421432{col 78}{space 3} .0241031
{txt}{space 20}UK  {c |}{col 25}{res}{space 2}        0{col 37}{txt}  (omitted)
{space 20}US  {c |}{col 25}{res}{space 2}        0{col 37}{txt}  (omitted)
{space 23} {c |}
{space 18}_cons {c |}{col 25}{res}{space 2} .1168522{col 37}{space 2} .0409574{col 48}{space 1}    2.85{col 57}{space 3}0.004{col 65}{space 4}  .036573{col 78}{space 3} .1971315
{txt}{hline 24}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{res}{txt}({res}est6{txt} stored)

{com}. estadd local CFE "X", replace

{txt}added macro:
                e(CFE) : "{res:X}"

{com}. estadd local Controls "X", replace

{txt}added macro:
           e(Controls) : "{res:X}"

{com}. test  TH1_TE1 TH1_TE2 TH1_TE3 TH1_TE4 TH2_TE1 TH2_TE2 TH2_TE3 TH2_TE4 TH3_TE1 TH3_TE2 TH3_TE3 TH3_TE4 TH4_TE1 TH4_TE2 TH4_TE3

{p 0 7}{space 1}{text:( 1)}{space 1} {res}TH1_TE1 = 0{p_end}
{p 0 7}{space 1}{text:( 2)}{space 1} TH1_TE2 = 0{p_end}
{p 0 7}{space 1}{text:( 3)}{space 1} TH1_TE3 = 0{p_end}
{p 0 7}{space 1}{text:( 4)}{space 1} TH1_TE4 = 0{p_end}
{p 0 7}{space 1}{text:( 5)}{space 1} TH2_TE1 = 0{p_end}
{p 0 7}{space 1}{text:( 6)}{space 1} TH2_TE2 = 0{p_end}
{p 0 7}{space 1}{text:( 7)}{space 1} TH2_TE3 = 0{p_end}
{p 0 7}{space 1}{text:( 8)}{space 1} TH2_TE4 = 0{p_end}
{p 0 7}{space 1}{text:( 9)}{space 1} TH3_TE1 = 0{p_end}
{p 0 7}{space 1}{text:(10)}{space 1} TH3_TE2 = 0{p_end}
{p 0 7}{space 1}{text:(11)}{space 1} TH3_TE3 = 0{p_end}
{p 0 7}{space 1}{text:(12)}{space 1} TH3_TE4 = 0{p_end}
{p 0 7}{space 1}{text:(13)}{space 1} TH4_TE1 = 0{p_end}
{p 0 7}{space 1}{text:(14)}{space 1} TH4_TE2 = 0{p_end}
{p 0 7}{space 1}{text:(15)}{space 1} TH4_TE3 = 0{p_end}

{txt}       F( 15, 22483) ={res}    2.89
{txt}{col 13}Prob > F ={res}    0.0001
{txt}
{com}. estadd scalar F = r(F), replace

{txt}added scalar:
                  e(F) =  {res}2.8893122
{txt}
{com}. 
. eststo: reg eval_eco healthc econc , robust

{txt}Linear regression                               Number of obs     = {res}    22,541
                                                {txt}F(2, 22538)       =  {res}    12.56
                                                {txt}Prob > F          = {res}    0.0000
                                                {txt}R-squared         = {res}    0.0011
                                                {txt}Root MSE          =    {res} .25831

{txt}{hline 13}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 14}{c |}{col 26}    Robust
{col 1}    eval_eco{col 14}{c |} Coefficient{col 26}  std. err.{col 38}      t{col 46}   P>|t|{col 54}     [95% con{col 67}f. interval]
{hline 13}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 5}healthc {c |}{col 14}{res}{space 2} .0067997{col 26}{space 2} .0043351{col 37}{space 1}    1.57{col 46}{space 3}0.117{col 54}{space 4}-.0016974{col 67}{space 3} .0152968
{txt}{space 7}econc {c |}{col 14}{res}{space 2} .0206556{col 26}{space 2} .0043461{col 37}{space 1}    4.75{col 46}{space 3}0.000{col 54}{space 4} .0121369{col 67}{space 3} .0291742
{txt}{space 7}_cons {c |}{col 14}{res}{space 2} .4879638{col 26}{space 2} .0035311{col 37}{space 1}  138.19{col 46}{space 3}0.000{col 54}{space 4} .4810426{col 67}{space 3}  .494885
{txt}{hline 13}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{res}{txt}({res}est7{txt} stored)

{com}. estadd scalar F = e(F), replace

{txt}added scalar:
                  e(F) =  {res}12.555428
{txt}
{com}. 
. eststo: reg eval_eco healthc econc $controls $mv_controls i.country, robust
{txt}{p 0 6 2}note: {bf:mv_thirties} omitted because of collinearity.{p_end}
{p 0 6 2}note: {bf:mv_fourties} omitted because of collinearity.{p_end}
{p 0 6 2}note: {bf:mv_fifties} omitted because of collinearity.{p_end}
{p 0 6 2}note: {bf:mv_sixties} omitted because of collinearity.{p_end}
{p 0 6 2}note: {bf:mv_seventies} omitted because of collinearity.{p_end}
{p 0 6 2}note: {bf:mv_income2quartile} omitted because of collinearity.{p_end}
{p 0 6 2}note: {bf:mv_income3quartile} omitted because of collinearity.{p_end}
{p 0 6 2}note: {bf:mv_income4quartile} omitted because of collinearity.{p_end}
{p 0 6 2}note: {bf:mv_female} omitted because of collinearity.{p_end}
{p 0 6 2}note: {bf:mv_college} omitted because of collinearity.{p_end}
{p 0 6 2}note: {bf:mv_christiannotcatholic} omitted because of collinearity.{p_end}
{p 0 6 2}note: {bf:mv_catholic} omitted because of collinearity.{p_end}
{p 0 6 2}note: {bf:mv_fulltimeworker} omitted because of collinearity.{p_end}
{p 0 6 2}note: {bf:mv_unemployed} omitted because of collinearity.{p_end}
{p 0 6 2}note: {bf:mv_outofLF} omitted because of collinearity.{p_end}
{p 0 6 2}note: {bf:mv_black} omitted because of collinearity.{p_end}
{p 0 6 2}note: {bf:mv_latino} omitted because of collinearity.{p_end}
{p 0 6 2}note: {bf:mv_asian} omitted because of collinearity.{p_end}
{p 0 6 2}note: {bf:mv_bluecollar} omitted because of collinearity.{p_end}
{p 0 6 2}note: {bf:mv_serviceworker} omitted because of collinearity.{p_end}
{p 0 6 2}note: {bf:4.country} omitted because of collinearity.{p_end}
{p 0 6 2}note: {bf:7.country} omitted because of collinearity.{p_end}
{p 0 6 2}note: {bf:9.country} omitted because of collinearity.{p_end}
{p 0 6 2}note: {bf:11.country} omitted because of collinearity.{p_end}
{p 0 6 2}note: {bf:12.country} omitted because of collinearity.{p_end}

Linear regression                               Number of obs     = {res}    22,541
                                                {txt}F(44, 22496)      =  {res}    55.01
                                                {txt}Prob > F          = {res}    0.0000
                                                {txt}R-squared         = {res}    0.0954
                                                {txt}Root MSE          =    {res} .24605

{txt}{hline 24}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 25}{c |}{col 37}    Robust
{col 1}               eval_eco{col 25}{c |} Coefficient{col 37}  std. err.{col 49}      t{col 57}   P>|t|{col 65}     [95% con{col 78}f. interval]
{hline 24}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 16}healthc {c |}{col 25}{res}{space 2} .0075214{col 37}{space 2} .0041276{col 48}{space 1}    1.82{col 57}{space 3}0.068{col 65}{space 4} -.000569{col 78}{space 3} .0156119
{txt}{space 18}econc {c |}{col 25}{res}{space 2} .0207043{col 37}{space 2} .0041434{col 48}{space 1}    5.00{col 57}{space 3}0.000{col 65}{space 4} .0125829{col 78}{space 3} .0288258
{txt}{space 15}thirties {c |}{col 25}{res}{space 2}-.0113523{col 37}{space 2} .0057216{col 48}{space 1}   -1.98{col 57}{space 3}0.047{col 65}{space 4} -.022567{col 78}{space 3}-.0001375
{txt}{space 15}fourties {c |}{col 25}{res}{space 2}-.0185732{col 37}{space 2} .0057064{col 48}{space 1}   -3.25{col 57}{space 3}0.001{col 65}{space 4}-.0297582{col 78}{space 3}-.0073882
{txt}{space 16}fifties {c |}{col 25}{res}{space 2}-.0133167{col 37}{space 2} .0059741{col 48}{space 1}   -2.23{col 57}{space 3}0.026{col 65}{space 4}-.0250264{col 78}{space 3}-.0016069
{txt}{space 16}sixties {c |}{col 25}{res}{space 2}  .000138{col 37}{space 2} .0059192{col 48}{space 1}    0.02{col 57}{space 3}0.981{col 65}{space 4}-.0114641{col 78}{space 3} .0117401
{txt}{space 14}seventies {c |}{col 25}{res}{space 2} .0199083{col 37}{space 2} .0070703{col 48}{space 1}    2.82{col 57}{space 3}0.005{col 65}{space 4} .0060499{col 78}{space 3} .0337666
{txt}{space 8}income2quartile {c |}{col 25}{res}{space 2} .0218952{col 37}{space 2} .0048792{col 48}{space 1}    4.49{col 57}{space 3}0.000{col 65}{space 4} .0123315{col 78}{space 3} .0314588
{txt}{space 8}income3quartile {c |}{col 25}{res}{space 2} .0238562{col 37}{space 2}  .005009{col 48}{space 1}    4.76{col 57}{space 3}0.000{col 65}{space 4} .0140382{col 78}{space 3} .0336742
{txt}{space 8}income4quartile {c |}{col 25}{res}{space 2}  .043704{col 37}{space 2} .0051795{col 48}{space 1}    8.44{col 57}{space 3}0.000{col 65}{space 4} .0335519{col 78}{space 3} .0538561
{txt}{space 9}incomenoanswer {c |}{col 25}{res}{space 2}-.0099118{col 37}{space 2} .0078327{col 48}{space 1}   -1.27{col 57}{space 3}0.206{col 65}{space 4}-.0252645{col 78}{space 3} .0054408
{txt}{space 17}female {c |}{col 25}{res}{space 2}-.0007603{col 37}{space 2} .0033743{col 48}{space 1}   -0.23{col 57}{space 3}0.822{col 65}{space 4}-.0073741{col 78}{space 3} .0058535
{txt}{space 13}highschool {c |}{col 25}{res}{space 2} .0016218{col 37}{space 2}  .005582{col 48}{space 1}    0.29{col 57}{space 3}0.771{col 65}{space 4}-.0093193{col 78}{space 3} .0125628
{txt}{space 16}college {c |}{col 25}{res}{space 2} .0011929{col 37}{space 2} .0052002{col 48}{space 1}    0.23{col 57}{space 3}0.819{col 65}{space 4}-.0089999{col 78}{space 3} .0113857
{txt}{space 13}noreligion {c |}{col 25}{res}{space 2}-.0175185{col 37}{space 2} .0071834{col 48}{space 1}   -2.44{col 57}{space 3}0.015{col 65}{space 4}-.0315985{col 78}{space 3}-.0034386
{txt}{space 3}christiannotcatholic {c |}{col 25}{res}{space 2} .0389688{col 37}{space 2} .0082896{col 48}{space 1}    4.70{col 57}{space 3}0.000{col 65}{space 4} .0227206{col 78}{space 3} .0552169
{txt}{space 15}catholic {c |}{col 25}{res}{space 2} .0170475{col 37}{space 2} .0073622{col 48}{space 1}    2.32{col 57}{space 3}0.021{col 65}{space 4} .0026171{col 78}{space 3}  .031478
{txt}{space 9}fulltimeworker {c |}{col 25}{res}{space 2}-.1283952{col 37}{space 2}  .008691{col 48}{space 1}  -14.77{col 57}{space 3}0.000{col 65}{space 4}-.1454302{col 78}{space 3}-.1113603
{txt}{space 9}parttimeworker {c |}{col 25}{res}{space 2}-.1240924{col 37}{space 2} .0210669{col 48}{space 1}   -5.89{col 57}{space 3}0.000{col 65}{space 4}-.1653849{col 78}{space 3}-.0827999
{txt}{space 13}unemployed {c |}{col 25}{res}{space 2}-.1596346{col 37}{space 2} .0134353{col 48}{space 1}  -11.88{col 57}{space 3}0.000{col 65}{space 4}-.1859686{col 78}{space 3}-.1333006
{txt}{space 11}selfemployed {c |}{col 25}{res}{space 2} -.145728{col 37}{space 2}  .037738{col 48}{space 1}   -3.86{col 57}{space 3}0.000{col 65}{space 4}-.2196971{col 78}{space 3}-.0717588
{txt}{space 16}outofLF {c |}{col 25}{res}{space 2}-.1375275{col 37}{space 2} .0092864{col 48}{space 1}  -14.81{col 57}{space 3}0.000{col 65}{space 4}-.1557296{col 78}{space 3}-.1193254
{txt}{space 13}goodhealth {c |}{col 25}{res}{space 2} .0393693{col 37}{space 2} .0035392{col 48}{space 1}   11.12{col 57}{space 3}0.000{col 65}{space 4} .0324322{col 78}{space 3} .0463064
{txt}{space 18}white {c |}{col 25}{res}{space 2} .0392949{col 37}{space 2} .0356402{col 48}{space 1}    1.10{col 57}{space 3}0.270{col 65}{space 4}-.0305625{col 78}{space 3} .1091522
{txt}{space 18}black {c |}{col 25}{res}{space 2} .0288372{col 37}{space 2} .0405814{col 48}{space 1}    0.71{col 57}{space 3}0.477{col 65}{space 4}-.0507052{col 78}{space 3} .1083797
{txt}{space 17}latino {c |}{col 25}{res}{space 2} .0296075{col 37}{space 2}  .044402{col 48}{space 1}    0.67{col 57}{space 3}0.505{col 65}{space 4}-.0574236{col 78}{space 3} .1166386
{txt}{space 18}asian {c |}{col 25}{res}{space 2}  .051432{col 37}{space 2} .0446186{col 48}{space 1}    1.15{col 57}{space 3}0.249{col 65}{space 4}-.0360235{col 78}{space 3} .1388875
{txt}{space 12}whitecollar {c |}{col 25}{res}{space 2}-.0125763{col 37}{space 2}  .008658{col 48}{space 1}   -1.45{col 57}{space 3}0.146{col 65}{space 4}-.0295466{col 78}{space 3} .0043941
{txt}{space 13}bluecollar {c |}{col 25}{res}{space 2}-.0219219{col 37}{space 2}  .009001{col 48}{space 1}   -2.44{col 57}{space 3}0.015{col 65}{space 4}-.0395645{col 78}{space 3}-.0042793
{txt}{space 10}serviceworker {c |}{col 25}{res}{space 2}-.0133032{col 37}{space 2} .0071792{col 48}{space 1}   -1.85{col 57}{space 3}0.064{col 65}{space 4}-.0273749{col 78}{space 3} .0007685
{txt}{space 12}mv_thirties {c |}{col 25}{res}{space 2}        0{col 37}{txt}  (omitted)
{space 12}mv_fourties {c |}{col 25}{res}{space 2}        0{col 37}{txt}  (omitted)
{space 13}mv_fifties {c |}{col 25}{res}{space 2}        0{col 37}{txt}  (omitted)
{space 13}mv_sixties {c |}{col 25}{res}{space 2}        0{col 37}{txt}  (omitted)
{space 11}mv_seventies {c |}{col 25}{res}{space 2}        0{col 37}{txt}  (omitted)
{space 5}mv_income2quartile {c |}{col 25}{res}{space 2}        0{col 37}{txt}  (omitted)
{space 5}mv_income3quartile {c |}{col 25}{res}{space 2}        0{col 37}{txt}  (omitted)
{space 5}mv_income4quartile {c |}{col 25}{res}{space 2}        0{col 37}{txt}  (omitted)
{space 6}mv_incomenoanswer {c |}{col 25}{res}{space 2} .0246236{col 37}{space 2} .0107737{col 48}{space 1}    2.29{col 57}{space 3}0.022{col 65}{space 4} .0035063{col 78}{space 3} .0457409
{txt}{space 14}mv_female {c |}{col 25}{res}{space 2}        0{col 37}{txt}  (omitted)
{space 10}mv_highschool {c |}{col 25}{res}{space 2}-.0053949{col 37}{space 2} .0136855{col 48}{space 1}   -0.39{col 57}{space 3}0.693{col 65}{space 4}-.0322194{col 78}{space 3} .0214296
{txt}{space 13}mv_college {c |}{col 25}{res}{space 2}        0{col 37}{txt}  (omitted)
{space 10}mv_noreligion {c |}{col 25}{res}{space 2} .0397641{col 37}{space 2} .0269367{col 48}{space 1}    1.48{col 57}{space 3}0.140{col 65}{space 4}-.0130337{col 78}{space 3}  .092562
{txt}mv_christiannotcatholic {c |}{col 25}{res}{space 2}        0{col 37}{txt}  (omitted)
{space 12}mv_catholic {c |}{col 25}{res}{space 2}        0{col 37}{txt}  (omitted)
{space 6}mv_fulltimeworker {c |}{col 25}{res}{space 2}        0{col 37}{txt}  (omitted)
{space 6}mv_parttimeworker {c |}{col 25}{res}{space 2} -.109593{col 37}{space 2}    .0165{col 48}{space 1}   -6.64{col 57}{space 3}0.000{col 65}{space 4}-.1419341{col 78}{space 3}-.0772519
{txt}{space 10}mv_unemployed {c |}{col 25}{res}{space 2}        0{col 37}{txt}  (omitted)
{space 8}mv_selfemployed {c |}{col 25}{res}{space 2} .1905487{col 37}{space 2} .0180132{col 48}{space 1}   10.58{col 57}{space 3}0.000{col 65}{space 4} .1552416{col 78}{space 3} .2258558
{txt}{space 13}mv_outofLF {c |}{col 25}{res}{space 2}        0{col 37}{txt}  (omitted)
{space 10}mv_goodhealth {c |}{col 25}{res}{space 2} .1236975{col 37}{space 2} .1000948{col 48}{space 1}    1.24{col 57}{space 3}0.217{col 65}{space 4}-.0724954{col 78}{space 3} .3198903
{txt}{space 15}mv_white {c |}{col 25}{res}{space 2} .2606139{col 37}{space 2} .0355428{col 48}{space 1}    7.33{col 57}{space 3}0.000{col 65}{space 4} .1909475{col 78}{space 3} .3302803
{txt}{space 15}mv_black {c |}{col 25}{res}{space 2}        0{col 37}{txt}  (omitted)
{space 14}mv_latino {c |}{col 25}{res}{space 2}        0{col 37}{txt}  (omitted)
{space 15}mv_asian {c |}{col 25}{res}{space 2}        0{col 37}{txt}  (omitted)
{space 9}mv_whitecollar {c |}{col 25}{res}{space 2} .0942666{col 37}{space 2} .0174944{col 48}{space 1}    5.39{col 57}{space 3}0.000{col 65}{space 4} .0599763{col 78}{space 3} .1285568
{txt}{space 10}mv_bluecollar {c |}{col 25}{res}{space 2}        0{col 37}{txt}  (omitted)
{space 7}mv_serviceworker {c |}{col 25}{res}{space 2}        0{col 37}{txt}  (omitted)
{space 23} {c |}
{space 16}country {c |}
{space 15}Austria  {c |}{col 25}{res}{space 2} .3635082{col 37}{space 2} .0394644{col 48}{space 1}    9.21{col 57}{space 3}0.000{col 65}{space 4} .2861553{col 78}{space 3} .4408611
{txt}{space 16}Brazil  {c |}{col 25}{res}{space 2}-.0624954{col 37}{space 2} .0178914{col 48}{space 1}   -3.49{col 57}{space 3}0.000{col 65}{space 4}-.0975637{col 78}{space 3} -.027427
{txt}{space 16}France  {c |}{col 25}{res}{space 2}        0{col 37}{txt}  (omitted)
{space 15}Germany  {c |}{col 25}{res}{space 2} .3648732{col 37}{space 2} .0390627{col 48}{space 1}    9.34{col 57}{space 3}0.000{col 65}{space 4} .2883076{col 78}{space 3} .4414388
{txt}{space 17}Italy  {c |}{col 25}{res}{space 2} .1445347{col 37}{space 2} .0473595{col 48}{space 1}    3.05{col 57}{space 3}0.002{col 65}{space 4} .0517067{col 78}{space 3} .2373627
{txt}{space 11}New_Zealand  {c |}{col 25}{res}{space 2}        0{col 37}{txt}  (omitted)
{space 16}Poland  {c |}{col 25}{res}{space 2}-.0762982{col 37}{space 2}   .01786{col 48}{space 1}   -4.27{col 57}{space 3}0.000{col 65}{space 4}-.1113051{col 78}{space 3}-.0412913
{txt}{space 17}Spain  {c |}{col 25}{res}{space 2}        0{col 37}{txt}  (omitted)
{space 16}Sweden  {c |}{col 25}{res}{space 2}-.0092877{col 37}{space 2} .0168932{col 48}{space 1}   -0.55{col 57}{space 3}0.582{col 65}{space 4}-.0423996{col 78}{space 3} .0238242
{txt}{space 20}UK  {c |}{col 25}{res}{space 2}        0{col 37}{txt}  (omitted)
{space 20}US  {c |}{col 25}{res}{space 2}        0{col 37}{txt}  (omitted)
{space 23} {c |}
{space 18}_cons {c |}{col 25}{res}{space 2}  .122958{col 37}{space 2} .0406629{col 48}{space 1}    3.02{col 57}{space 3}0.002{col 65}{space 4} .0432558{col 78}{space 3} .2026601
{txt}{hline 24}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{res}{txt}({res}est8{txt} stored)

{com}. estadd local CFE "X", replace

{txt}added macro:
                e(CFE) : "{res:X}"

{com}. estadd local Controls "X", replace

{txt}added macro:
           e(Controls) : "{res:X}"

{com}. test  healthc econc

{p 0 7}{space 1}{text:( 1)}{space 1} {res}healthc = 0{p_end}
{p 0 7}{space 1}{text:( 2)}{space 1} econc = 0{p_end}

{txt}       F(  2, 22496) ={res}   14.25
{txt}{col 13}Prob > F ={res}    0.0000
{txt}
{com}. estadd scalar F = r(F), replace

{txt}added scalar:
                  e(F) =  {res}14.246936
{txt}
{com}. 
. esttab using "3_output/2_OA/Tables/TableE5.tex", depvar keep(TH* healthc econc) cons ///
> label replace order(_cons healthc econc TH1_TE1 TH1_TE2 TH1_TE3 TH1_TE4 TH2_TE1 TH2_TE2 TH2_TE3 TH2_TE4 TH3_TE1 TH3_TE2 TH3_TE3 TH3_TE4 TH4_TE1 TH4_TE2 TH4_TE3) ///
>         cells(b(star fmt(%15.3fc)) se(par fmt(%15.3fc))) interaction(" $\times$ ") ///
>         star(* 0.10 ** 0.05 *** 0.01) ///
>         stats(Controls CFE N  F , layout(@ @ @ @  ) fmt(%15s %15s %15.0fc %9.3f) labels("Controls" "Country FE" Observations  "F-statistic")) ///
>         collabels(none) mlabels(none) ///
>         mgroups("Health satisfaction" "Economic satisfaction" , pattern(1 0 0 0 1 0 0 0) ///
>         prefix(\multicolumn{c -(}@span{c )-}{c -(}c{c )-}{c -(}) suffix({c )-}) span erepeat(\cmidrule(lr){c -(}@span{c )-}))
{res}{txt}(output written to {browse  `"3_output/2_OA/Tables/TableE5.tex"'})

{com}. 
.                 
. **# Table E.6. Satisfaction with the head of government, first stage.
. eststo clear
{txt}
{com}. eststo: reg satis_head TH1_TE1 TH1_TE2 TH1_TE3 TH1_TE4 TH2_TE1 TH2_TE2 TH2_TE3 TH2_TE4 TH3_TE1 TH3_TE2 TH3_TE3 TH3_TE4 TH4_TE1 TH4_TE2 TH4_TE3, robust

{txt}Linear regression                               Number of obs     = {res}    22,541
                                                {txt}F(15, 22525)      =  {res}     1.33
                                                {txt}Prob > F          = {res}    0.1750
                                                {txt}R-squared         = {res}    0.0009
                                                {txt}Root MSE          =    {res} .31403

{txt}{hline 13}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 14}{c |}{col 26}    Robust
{col 1}  satis_head{col 14}{c |} Coefficient{col 26}  std. err.{col 38}      t{col 46}   P>|t|{col 54}     [95% con{col 67}f. interval]
{hline 13}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 5}TH1_TE1 {c |}{col 14}{res}{space 2} .0109778{col 26}{space 2} .0119189{col 37}{space 1}    0.92{col 46}{space 3}0.357{col 54}{space 4}-.0123839{col 67}{space 3} .0343396
{txt}{space 5}TH1_TE2 {c |}{col 14}{res}{space 2} .0167497{col 26}{space 2} .0118741{col 37}{space 1}    1.41{col 46}{space 3}0.158{col 54}{space 4}-.0065243{col 67}{space 3} .0400236
{txt}{space 5}TH1_TE3 {c |}{col 14}{res}{space 2} .0082375{col 26}{space 2} .0118984{col 37}{space 1}    0.69{col 46}{space 3}0.489{col 54}{space 4}-.0150842{col 67}{space 3} .0315592
{txt}{space 5}TH1_TE4 {c |}{col 14}{res}{space 2} .0215686{col 26}{space 2} .0118527{col 37}{space 1}    1.82{col 46}{space 3}0.069{col 54}{space 4}-.0016635{col 67}{space 3} .0448008
{txt}{space 5}TH2_TE1 {c |}{col 14}{res}{space 2} .0291677{col 26}{space 2} .0117533{col 37}{space 1}    2.48{col 46}{space 3}0.013{col 54}{space 4} .0061305{col 67}{space 3} .0522049
{txt}{space 5}TH2_TE2 {c |}{col 14}{res}{space 2} .0413661{col 26}{space 2} .0117789{col 37}{space 1}    3.51{col 46}{space 3}0.000{col 54}{space 4} .0182786{col 67}{space 3} .0644536
{txt}{space 5}TH2_TE3 {c |}{col 14}{res}{space 2} .0177257{col 26}{space 2} .0117396{col 37}{space 1}    1.51{col 46}{space 3}0.131{col 54}{space 4}-.0052847{col 67}{space 3} .0407361
{txt}{space 5}TH2_TE4 {c |}{col 14}{res}{space 2} .0168939{col 26}{space 2} .0117372{col 37}{space 1}    1.44{col 46}{space 3}0.150{col 54}{space 4}-.0061117{col 67}{space 3} .0398996
{txt}{space 5}TH3_TE1 {c |}{col 14}{res}{space 2} .0143246{col 26}{space 2} .0119708{col 37}{space 1}    1.20{col 46}{space 3}0.231{col 54}{space 4}-.0091391{col 67}{space 3} .0377883
{txt}{space 5}TH3_TE2 {c |}{col 14}{res}{space 2} .0197948{col 26}{space 2} .0118526{col 37}{space 1}    1.67{col 46}{space 3}0.095{col 54}{space 4}-.0034372{col 67}{space 3} .0430267
{txt}{space 5}TH3_TE3 {c |}{col 14}{res}{space 2} .0104963{col 26}{space 2} .0119905{col 37}{space 1}    0.88{col 46}{space 3}0.381{col 54}{space 4}-.0130058{col 67}{space 3} .0339985
{txt}{space 5}TH3_TE4 {c |}{col 14}{res}{space 2} .0129819{col 26}{space 2} .0118064{col 37}{space 1}    1.10{col 46}{space 3}0.272{col 54}{space 4}-.0101595{col 67}{space 3} .0361233
{txt}{space 5}TH4_TE1 {c |}{col 14}{res}{space 2} .0164391{col 26}{space 2} .0117951{col 37}{space 1}    1.39{col 46}{space 3}0.163{col 54}{space 4}-.0066801{col 67}{space 3} .0395583
{txt}{space 5}TH4_TE2 {c |}{col 14}{res}{space 2}  .004561{col 26}{space 2} .0119805{col 37}{space 1}    0.38{col 46}{space 3}0.703{col 54}{space 4}-.0189217{col 67}{space 3} .0280436
{txt}{space 5}TH4_TE3 {c |}{col 14}{res}{space 2} .0154682{col 26}{space 2} .0118798{col 37}{space 1}    1.30{col 46}{space 3}0.193{col 54}{space 4} -.007817{col 67}{space 3} .0387534
{txt}{space 7}_cons {c |}{col 14}{res}{space 2} .4421201{col 26}{space 2}  .008401{col 37}{space 1}   52.63{col 46}{space 3}0.000{col 54}{space 4} .4256536{col 67}{space 3} .4585866
{txt}{hline 13}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{res}{txt}({res}est1{txt} stored)

{com}. estadd scalar F = e(F), replace

{txt}added scalar:
                  e(F) =  {res}1.3283149
{txt}
{com}. 
. eststo: reg satis_head TH1_TE1 TH1_TE2 TH1_TE3 TH1_TE4 TH2_TE1 TH2_TE2 TH2_TE3 TH2_TE4 TH3_TE1 TH3_TE2 TH3_TE3 TH3_TE4 TH4_TE1 TH4_TE2 TH4_TE3 $controls $mv_controls i.country, robust
{txt}{p 0 6 2}note: {bf:mv_thirties} omitted because of collinearity.{p_end}
{p 0 6 2}note: {bf:mv_fourties} omitted because of collinearity.{p_end}
{p 0 6 2}note: {bf:mv_fifties} omitted because of collinearity.{p_end}
{p 0 6 2}note: {bf:mv_sixties} omitted because of collinearity.{p_end}
{p 0 6 2}note: {bf:mv_seventies} omitted because of collinearity.{p_end}
{p 0 6 2}note: {bf:mv_income2quartile} omitted because of collinearity.{p_end}
{p 0 6 2}note: {bf:mv_income3quartile} omitted because of collinearity.{p_end}
{p 0 6 2}note: {bf:mv_income4quartile} omitted because of collinearity.{p_end}
{p 0 6 2}note: {bf:mv_female} omitted because of collinearity.{p_end}
{p 0 6 2}note: {bf:mv_college} omitted because of collinearity.{p_end}
{p 0 6 2}note: {bf:mv_christiannotcatholic} omitted because of collinearity.{p_end}
{p 0 6 2}note: {bf:mv_catholic} omitted because of collinearity.{p_end}
{p 0 6 2}note: {bf:mv_fulltimeworker} omitted because of collinearity.{p_end}
{p 0 6 2}note: {bf:mv_unemployed} omitted because of collinearity.{p_end}
{p 0 6 2}note: {bf:mv_outofLF} omitted because of collinearity.{p_end}
{p 0 6 2}note: {bf:mv_black} omitted because of collinearity.{p_end}
{p 0 6 2}note: {bf:mv_latino} omitted because of collinearity.{p_end}
{p 0 6 2}note: {bf:mv_asian} omitted because of collinearity.{p_end}
{p 0 6 2}note: {bf:mv_bluecollar} omitted because of collinearity.{p_end}
{p 0 6 2}note: {bf:mv_serviceworker} omitted because of collinearity.{p_end}
{p 0 6 2}note: {bf:4.country} omitted because of collinearity.{p_end}
{p 0 6 2}note: {bf:7.country} omitted because of collinearity.{p_end}
{p 0 6 2}note: {bf:9.country} omitted because of collinearity.{p_end}
{p 0 6 2}note: {bf:11.country} omitted because of collinearity.{p_end}
{p 0 6 2}note: {bf:12.country} omitted because of collinearity.{p_end}

Linear regression                               Number of obs     = {res}    22,541
                                                {txt}F(57, 22483)      =  {res}    60.51
                                                {txt}Prob > F          = {res}    0.0000
                                                {txt}R-squared         = {res}    0.1204
                                                {txt}Root MSE          =    {res} .29493

{txt}{hline 24}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 25}{c |}{col 37}    Robust
{col 1}             satis_head{col 25}{c |} Coefficient{col 37}  std. err.{col 49}      t{col 57}   P>|t|{col 65}     [95% con{col 78}f. interval]
{hline 24}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 16}TH1_TE1 {c |}{col 25}{res}{space 2}  .012596{col 37}{space 2} .0111818{col 48}{space 1}    1.13{col 57}{space 3}0.260{col 65}{space 4} -.009321{col 78}{space 3} .0345131
{txt}{space 16}TH1_TE2 {c |}{col 25}{res}{space 2}  .017628{col 37}{space 2} .0112388{col 48}{space 1}    1.57{col 57}{space 3}0.117{col 65}{space 4}-.0044007{col 78}{space 3} .0396568
{txt}{space 16}TH1_TE3 {c |}{col 25}{res}{space 2} .0079947{col 37}{space 2} .0111121{col 48}{space 1}    0.72{col 57}{space 3}0.472{col 65}{space 4}-.0137858{col 78}{space 3} .0297752
{txt}{space 16}TH1_TE4 {c |}{col 25}{res}{space 2} .0220753{col 37}{space 2} .0111653{col 48}{space 1}    1.98{col 57}{space 3}0.048{col 65}{space 4} .0001906{col 78}{space 3} .0439599
{txt}{space 16}TH2_TE1 {c |}{col 25}{res}{space 2} .0297361{col 37}{space 2} .0110764{col 48}{space 1}    2.68{col 57}{space 3}0.007{col 65}{space 4} .0080256{col 78}{space 3} .0514467
{txt}{space 16}TH2_TE2 {c |}{col 25}{res}{space 2} .0414533{col 37}{space 2} .0110042{col 48}{space 1}    3.77{col 57}{space 3}0.000{col 65}{space 4} .0198844{col 78}{space 3} .0630222
{txt}{space 16}TH2_TE3 {c |}{col 25}{res}{space 2} .0201397{col 37}{space 2} .0110314{col 48}{space 1}    1.83{col 57}{space 3}0.068{col 65}{space 4}-.0014827{col 78}{space 3} .0417621
{txt}{space 16}TH2_TE4 {c |}{col 25}{res}{space 2} .0180055{col 37}{space 2} .0111761{col 48}{space 1}    1.61{col 57}{space 3}0.107{col 65}{space 4}-.0039004{col 78}{space 3} .0399115
{txt}{space 16}TH3_TE1 {c |}{col 25}{res}{space 2} .0160677{col 37}{space 2} .0111922{col 48}{space 1}    1.44{col 57}{space 3}0.151{col 65}{space 4}-.0058698{col 78}{space 3} .0380051
{txt}{space 16}TH3_TE2 {c |}{col 25}{res}{space 2} .0233918{col 37}{space 2} .0111545{col 48}{space 1}    2.10{col 57}{space 3}0.036{col 65}{space 4} .0015282{col 78}{space 3} .0452555
{txt}{space 16}TH3_TE3 {c |}{col 25}{res}{space 2} .0103508{col 37}{space 2} .0112019{col 48}{space 1}    0.92{col 57}{space 3}0.355{col 65}{space 4}-.0116057{col 78}{space 3} .0323073
{txt}{space 16}TH3_TE4 {c |}{col 25}{res}{space 2}  .015314{col 37}{space 2} .0110737{col 48}{space 1}    1.38{col 57}{space 3}0.167{col 65}{space 4}-.0063912{col 78}{space 3} .0370192
{txt}{space 16}TH4_TE1 {c |}{col 25}{res}{space 2} .0153762{col 37}{space 2} .0111646{col 48}{space 1}    1.38{col 57}{space 3}0.168{col 65}{space 4}-.0065072{col 78}{space 3} .0372596
{txt}{space 16}TH4_TE2 {c |}{col 25}{res}{space 2} .0045384{col 37}{space 2}  .011154{col 48}{space 1}    0.41{col 57}{space 3}0.684{col 65}{space 4}-.0173242{col 78}{space 3} .0264009
{txt}{space 16}TH4_TE3 {c |}{col 25}{res}{space 2} .0163346{col 37}{space 2} .0111302{col 48}{space 1}    1.47{col 57}{space 3}0.142{col 65}{space 4}-.0054813{col 78}{space 3} .0381505
{txt}{space 15}thirties {c |}{col 25}{res}{space 2}-.0134461{col 37}{space 2} .0067523{col 48}{space 1}   -1.99{col 57}{space 3}0.046{col 65}{space 4}-.0266811{col 78}{space 3}-.0002112
{txt}{space 15}fourties {c |}{col 25}{res}{space 2}-.0315238{col 37}{space 2}  .006775{col 48}{space 1}   -4.65{col 57}{space 3}0.000{col 65}{space 4}-.0448033{col 78}{space 3}-.0182443
{txt}{space 16}fifties {c |}{col 25}{res}{space 2}-.0337942{col 37}{space 2} .0071035{col 48}{space 1}   -4.76{col 57}{space 3}0.000{col 65}{space 4}-.0477175{col 78}{space 3}-.0198708
{txt}{space 16}sixties {c |}{col 25}{res}{space 2}-.0356444{col 37}{space 2} .0071346{col 48}{space 1}   -5.00{col 57}{space 3}0.000{col 65}{space 4}-.0496288{col 78}{space 3}  -.02166
{txt}{space 14}seventies {c |}{col 25}{res}{space 2}-.0136798{col 37}{space 2} .0086987{col 48}{space 1}   -1.57{col 57}{space 3}0.116{col 65}{space 4}-.0307299{col 78}{space 3} .0033702
{txt}{space 8}income2quartile {c |}{col 25}{res}{space 2} .0170704{col 37}{space 2} .0058072{col 48}{space 1}    2.94{col 57}{space 3}0.003{col 65}{space 4} .0056879{col 78}{space 3} .0284529
{txt}{space 8}income3quartile {c |}{col 25}{res}{space 2} .0229709{col 37}{space 2} .0059937{col 48}{space 1}    3.83{col 57}{space 3}0.000{col 65}{space 4} .0112228{col 78}{space 3} .0347189
{txt}{space 8}income4quartile {c |}{col 25}{res}{space 2} .0414671{col 37}{space 2} .0061392{col 48}{space 1}    6.75{col 57}{space 3}0.000{col 65}{space 4} .0294339{col 78}{space 3} .0535004
{txt}{space 9}incomenoanswer {c |}{col 25}{res}{space 2}-.0095248{col 37}{space 2} .0093958{col 48}{space 1}   -1.01{col 57}{space 3}0.311{col 65}{space 4}-.0279413{col 78}{space 3} .0088916
{txt}{space 17}female {c |}{col 25}{res}{space 2} .0108137{col 37}{space 2} .0040352{col 48}{space 1}    2.68{col 57}{space 3}0.007{col 65}{space 4} .0029045{col 78}{space 3} .0187229
{txt}{space 13}highschool {c |}{col 25}{res}{space 2} .0091315{col 37}{space 2} .0065604{col 48}{space 1}    1.39{col 57}{space 3}0.164{col 65}{space 4}-.0037273{col 78}{space 3} .0219903
{txt}{space 16}college {c |}{col 25}{res}{space 2} .0023106{col 37}{space 2} .0061147{col 48}{space 1}    0.38{col 57}{space 3}0.706{col 65}{space 4}-.0096746{col 78}{space 3} .0142959
{txt}{space 13}noreligion {c |}{col 25}{res}{space 2}-.0310513{col 37}{space 2} .0084167{col 48}{space 1}   -3.69{col 57}{space 3}0.000{col 65}{space 4}-.0475486{col 78}{space 3} -.014554
{txt}{space 3}christiannotcatholic {c |}{col 25}{res}{space 2} .0642133{col 37}{space 2} .0098339{col 48}{space 1}    6.53{col 57}{space 3}0.000{col 65}{space 4} .0449382{col 78}{space 3} .0834885
{txt}{space 15}catholic {c |}{col 25}{res}{space 2} .0216638{col 37}{space 2} .0086285{col 48}{space 1}    2.51{col 57}{space 3}0.012{col 65}{space 4} .0047512{col 78}{space 3} .0385763
{txt}{space 9}fulltimeworker {c |}{col 25}{res}{space 2}-.3125038{col 37}{space 2} .0097969{col 48}{space 1}  -31.90{col 57}{space 3}0.000{col 65}{space 4}-.3317064{col 78}{space 3}-.2933013
{txt}{space 9}parttimeworker {c |}{col 25}{res}{space 2}-.3046526{col 37}{space 2} .0248884{col 48}{space 1}  -12.24{col 57}{space 3}0.000{col 65}{space 4}-.3534356{col 78}{space 3}-.2558696
{txt}{space 13}unemployed {c |}{col 25}{res}{space 2}-.3460549{col 37}{space 2} .0152919{col 48}{space 1}  -22.63{col 57}{space 3}0.000{col 65}{space 4}-.3760281{col 78}{space 3}-.3160817
{txt}{space 11}selfemployed {c |}{col 25}{res}{space 2}-.3688705{col 37}{space 2} .0386966{col 48}{space 1}   -9.53{col 57}{space 3}0.000{col 65}{space 4}-.4447185{col 78}{space 3}-.2930225
{txt}{space 16}outofLF {c |}{col 25}{res}{space 2}-.3244391{col 37}{space 2} .0105882{col 48}{space 1}  -30.64{col 57}{space 3}0.000{col 65}{space 4}-.3451927{col 78}{space 3}-.3036854
{txt}{space 13}goodhealth {c |}{col 25}{res}{space 2} .0501941{col 37}{space 2} .0042225{col 48}{space 1}   11.89{col 57}{space 3}0.000{col 65}{space 4} .0419177{col 78}{space 3} .0584704
{txt}{space 18}white {c |}{col 25}{res}{space 2} .1320079{col 37}{space 2} .0430012{col 48}{space 1}    3.07{col 57}{space 3}0.002{col 65}{space 4} .0477226{col 78}{space 3} .2162932
{txt}{space 18}black {c |}{col 25}{res}{space 2}  .011632{col 37}{space 2} .0478422{col 48}{space 1}    0.24{col 57}{space 3}0.808{col 65}{space 4}-.0821421{col 78}{space 3}  .105406
{txt}{space 17}latino {c |}{col 25}{res}{space 2} .0360456{col 37}{space 2} .0530417{col 48}{space 1}    0.68{col 57}{space 3}0.497{col 65}{space 4}-.0679198{col 78}{space 3} .1400111
{txt}{space 18}asian {c |}{col 25}{res}{space 2}  .073065{col 37}{space 2} .0531737{col 48}{space 1}    1.37{col 57}{space 3}0.169{col 65}{space 4}-.0311591{col 78}{space 3} .1772891
{txt}{space 12}whitecollar {c |}{col 25}{res}{space 2}-.0171457{col 37}{space 2}  .010654{col 48}{space 1}   -1.61{col 57}{space 3}0.108{col 65}{space 4}-.0380283{col 78}{space 3} .0037368
{txt}{space 13}bluecollar {c |}{col 25}{res}{space 2}-.0234694{col 37}{space 2} .0109823{col 48}{space 1}   -2.14{col 57}{space 3}0.033{col 65}{space 4}-.0449954{col 78}{space 3}-.0019434
{txt}{space 10}serviceworker {c |}{col 25}{res}{space 2}-.0099858{col 37}{space 2} .0087792{col 48}{space 1}   -1.14{col 57}{space 3}0.255{col 65}{space 4}-.0271936{col 78}{space 3}  .007222
{txt}{space 12}mv_thirties {c |}{col 25}{res}{space 2}        0{col 37}{txt}  (omitted)
{space 12}mv_fourties {c |}{col 25}{res}{space 2}        0{col 37}{txt}  (omitted)
{space 13}mv_fifties {c |}{col 25}{res}{space 2}        0{col 37}{txt}  (omitted)
{space 13}mv_sixties {c |}{col 25}{res}{space 2}        0{col 37}{txt}  (omitted)
{space 11}mv_seventies {c |}{col 25}{res}{space 2}        0{col 37}{txt}  (omitted)
{space 5}mv_income2quartile {c |}{col 25}{res}{space 2}        0{col 37}{txt}  (omitted)
{space 5}mv_income3quartile {c |}{col 25}{res}{space 2}        0{col 37}{txt}  (omitted)
{space 5}mv_income4quartile {c |}{col 25}{res}{space 2}        0{col 37}{txt}  (omitted)
{space 6}mv_incomenoanswer {c |}{col 25}{res}{space 2}-.0743021{col 37}{space 2} .0122995{col 48}{space 1}   -6.04{col 57}{space 3}0.000{col 65}{space 4}  -.09841{col 78}{space 3}-.0501942
{txt}{space 14}mv_female {c |}{col 25}{res}{space 2}        0{col 37}{txt}  (omitted)
{space 10}mv_highschool {c |}{col 25}{res}{space 2}-.0105852{col 37}{space 2} .0157547{col 48}{space 1}   -0.67{col 57}{space 3}0.502{col 65}{space 4}-.0414655{col 78}{space 3}  .020295
{txt}{space 13}mv_college {c |}{col 25}{res}{space 2}        0{col 37}{txt}  (omitted)
{space 10}mv_noreligion {c |}{col 25}{res}{space 2} .0047749{col 37}{space 2} .0289338{col 48}{space 1}    0.17{col 57}{space 3}0.869{col 65}{space 4}-.0519373{col 78}{space 3} .0614872
{txt}mv_christiannotcatholic {c |}{col 25}{res}{space 2}        0{col 37}{txt}  (omitted)
{space 12}mv_catholic {c |}{col 25}{res}{space 2}        0{col 37}{txt}  (omitted)
{space 6}mv_fulltimeworker {c |}{col 25}{res}{space 2}        0{col 37}{txt}  (omitted)
{space 6}mv_parttimeworker {c |}{col 25}{res}{space 2}-.2887253{col 37}{space 2} .0196955{col 48}{space 1}  -14.66{col 57}{space 3}0.000{col 65}{space 4}-.3273299{col 78}{space 3}-.2501208
{txt}{space 10}mv_unemployed {c |}{col 25}{res}{space 2}        0{col 37}{txt}  (omitted)
{space 8}mv_selfemployed {c |}{col 25}{res}{space 2} .2967151{col 37}{space 2} .0213147{col 48}{space 1}   13.92{col 57}{space 3}0.000{col 65}{space 4} .2549368{col 78}{space 3} .3384934
{txt}{space 13}mv_outofLF {c |}{col 25}{res}{space 2}        0{col 37}{txt}  (omitted)
{space 10}mv_goodhealth {c |}{col 25}{res}{space 2} .1711966{col 37}{space 2} .0719131{col 48}{space 1}    2.38{col 57}{space 3}0.017{col 65}{space 4}  .030242{col 78}{space 3} .3121512
{txt}{space 15}mv_white {c |}{col 25}{res}{space 2} .3810651{col 37}{space 2} .0427033{col 48}{space 1}    8.92{col 57}{space 3}0.000{col 65}{space 4} .2973636{col 78}{space 3} .4647666
{txt}{space 15}mv_black {c |}{col 25}{res}{space 2}        0{col 37}{txt}  (omitted)
{space 14}mv_latino {c |}{col 25}{res}{space 2}        0{col 37}{txt}  (omitted)
{space 15}mv_asian {c |}{col 25}{res}{space 2}        0{col 37}{txt}  (omitted)
{space 9}mv_whitecollar {c |}{col 25}{res}{space 2} .3040286{col 37}{space 2}  .020589{col 48}{space 1}   14.77{col 57}{space 3}0.000{col 65}{space 4} .2636727{col 78}{space 3} .3443845
{txt}{space 10}mv_bluecollar {c |}{col 25}{res}{space 2}        0{col 37}{txt}  (omitted)
{space 7}mv_serviceworker {c |}{col 25}{res}{space 2}        0{col 37}{txt}  (omitted)
{space 23} {c |}
{space 16}country {c |}
{space 15}Austria  {c |}{col 25}{res}{space 2} .5160263{col 37}{space 2} .0473959{col 48}{space 1}   10.89{col 57}{space 3}0.000{col 65}{space 4}  .423127{col 78}{space 3} .6089255
{txt}{space 16}Brazil  {c |}{col 25}{res}{space 2} -.015539{col 37}{space 2} .0213489{col 48}{space 1}   -0.73{col 57}{space 3}0.467{col 65}{space 4}-.0573843{col 78}{space 3} .0263062
{txt}{space 16}France  {c |}{col 25}{res}{space 2}        0{col 37}{txt}  (omitted)
{space 15}Germany  {c |}{col 25}{res}{space 2} .5549103{col 37}{space 2} .0468381{col 48}{space 1}   11.85{col 57}{space 3}0.000{col 65}{space 4} .4631045{col 78}{space 3} .6467162
{txt}{space 17}Italy  {c |}{col 25}{res}{space 2} .4906842{col 37}{space 2} .0548277{col 48}{space 1}    8.95{col 57}{space 3}0.000{col 65}{space 4} .3832181{col 78}{space 3} .5981503
{txt}{space 11}New_Zealand  {c |}{col 25}{res}{space 2}        0{col 37}{txt}  (omitted)
{space 16}Poland  {c |}{col 25}{res}{space 2}-.0339249{col 37}{space 2} .0214545{col 48}{space 1}   -1.58{col 57}{space 3}0.114{col 65}{space 4}-.0759772{col 78}{space 3} .0081275
{txt}{space 17}Spain  {c |}{col 25}{res}{space 2}        0{col 37}{txt}  (omitted)
{space 16}Sweden  {c |}{col 25}{res}{space 2} .0591902{col 37}{space 2} .0204504{col 48}{space 1}    2.89{col 57}{space 3}0.004{col 65}{space 4}  .019106{col 78}{space 3} .0992745
{txt}{space 20}UK  {c |}{col 25}{res}{space 2}        0{col 37}{txt}  (omitted)
{space 20}US  {c |}{col 25}{res}{space 2}        0{col 37}{txt}  (omitted)
{space 23} {c |}
{space 18}_cons {c |}{col 25}{res}{space 2}-.0262489{col 37}{space 2} .0491121{col 48}{space 1}   -0.53{col 57}{space 3}0.593{col 65}{space 4} -.122512{col 78}{space 3} .0700141
{txt}{hline 24}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{res}{txt}({res}est2{txt} stored)

{com}. estadd local CFE "X", replace

{txt}added macro:
                e(CFE) : "{res:X}"

{com}. estadd local Controls "X", replace

{txt}added macro:
           e(Controls) : "{res:X}"

{com}. test  TH1_TE1 TH1_TE2 TH1_TE3 TH1_TE4 TH2_TE1 TH2_TE2 TH2_TE3 TH2_TE4 TH3_TE1 TH3_TE2 TH3_TE3 TH3_TE4 TH4_TE1 TH4_TE2 TH4_TE3

{p 0 7}{space 1}{text:( 1)}{space 1} {res}TH1_TE1 = 0{p_end}
{p 0 7}{space 1}{text:( 2)}{space 1} TH1_TE2 = 0{p_end}
{p 0 7}{space 1}{text:( 3)}{space 1} TH1_TE3 = 0{p_end}
{p 0 7}{space 1}{text:( 4)}{space 1} TH1_TE4 = 0{p_end}
{p 0 7}{space 1}{text:( 5)}{space 1} TH2_TE1 = 0{p_end}
{p 0 7}{space 1}{text:( 6)}{space 1} TH2_TE2 = 0{p_end}
{p 0 7}{space 1}{text:( 7)}{space 1} TH2_TE3 = 0{p_end}
{p 0 7}{space 1}{text:( 8)}{space 1} TH2_TE4 = 0{p_end}
{p 0 7}{space 1}{text:( 9)}{space 1} TH3_TE1 = 0{p_end}
{p 0 7}{space 1}{text:(10)}{space 1} TH3_TE2 = 0{p_end}
{p 0 7}{space 1}{text:(11)}{space 1} TH3_TE3 = 0{p_end}
{p 0 7}{space 1}{text:(12)}{space 1} TH3_TE4 = 0{p_end}
{p 0 7}{space 1}{text:(13)}{space 1} TH4_TE1 = 0{p_end}
{p 0 7}{space 1}{text:(14)}{space 1} TH4_TE2 = 0{p_end}
{p 0 7}{space 1}{text:(15)}{space 1} TH4_TE3 = 0{p_end}

{txt}       F( 15, 22483) ={res}    1.58
{txt}{col 13}Prob > F ={res}    0.0710
{txt}
{com}. estadd scalar F = r(F), replace

{txt}added scalar:
                  e(F) =  {res}1.5781581
{txt}
{com}. 
. eststo: reg satis_head healthc econc, robust

{txt}Linear regression                               Number of obs     = {res}    22,541
                                                {txt}F(2, 22538)       =  {res}     4.57
                                                {txt}Prob > F          = {res}    0.0103
                                                {txt}R-squared         = {res}    0.0004
                                                {txt}Root MSE          =    {res} .31402

{txt}{hline 13}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 14}{c |}{col 26}    Robust
{col 1}  satis_head{col 14}{c |} Coefficient{col 26}  std. err.{col 38}      t{col 46}   P>|t|{col 54}     [95% con{col 67}f. interval]
{hline 13}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 5}healthc {c |}{col 14}{res}{space 2}  .013699{col 26}{space 2} .0052706{col 37}{space 1}    2.60{col 46}{space 3}0.009{col 54}{space 4} .0033683{col 67}{space 3} .0240298
{txt}{space 7}econc {c |}{col 14}{res}{space 2} .0081712{col 26}{space 2} .0052851{col 37}{space 1}    1.55{col 46}{space 3}0.122{col 54}{space 4} -.002188{col 67}{space 3} .0185305
{txt}{space 7}_cons {c |}{col 14}{res}{space 2} .4472688{col 26}{space 2} .0042913{col 37}{space 1}  104.23{col 46}{space 3}0.000{col 54}{space 4} .4388576{col 67}{space 3}   .45568
{txt}{hline 13}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{res}{txt}({res}est3{txt} stored)

{com}. estadd scalar F = e(F), replace

{txt}added scalar:
                  e(F) =  {res}4.5733259
{txt}
{com}. 
. eststo: reg satis_head healthc econc $controls $mv_controls i.country, robust
{txt}{p 0 6 2}note: {bf:mv_thirties} omitted because of collinearity.{p_end}
{p 0 6 2}note: {bf:mv_fourties} omitted because of collinearity.{p_end}
{p 0 6 2}note: {bf:mv_fifties} omitted because of collinearity.{p_end}
{p 0 6 2}note: {bf:mv_sixties} omitted because of collinearity.{p_end}
{p 0 6 2}note: {bf:mv_seventies} omitted because of collinearity.{p_end}
{p 0 6 2}note: {bf:mv_income2quartile} omitted because of collinearity.{p_end}
{p 0 6 2}note: {bf:mv_income3quartile} omitted because of collinearity.{p_end}
{p 0 6 2}note: {bf:mv_income4quartile} omitted because of collinearity.{p_end}
{p 0 6 2}note: {bf:mv_female} omitted because of collinearity.{p_end}
{p 0 6 2}note: {bf:mv_college} omitted because of collinearity.{p_end}
{p 0 6 2}note: {bf:mv_christiannotcatholic} omitted because of collinearity.{p_end}
{p 0 6 2}note: {bf:mv_catholic} omitted because of collinearity.{p_end}
{p 0 6 2}note: {bf:mv_fulltimeworker} omitted because of collinearity.{p_end}
{p 0 6 2}note: {bf:mv_unemployed} omitted because of collinearity.{p_end}
{p 0 6 2}note: {bf:mv_outofLF} omitted because of collinearity.{p_end}
{p 0 6 2}note: {bf:mv_black} omitted because of collinearity.{p_end}
{p 0 6 2}note: {bf:mv_latino} omitted because of collinearity.{p_end}
{p 0 6 2}note: {bf:mv_asian} omitted because of collinearity.{p_end}
{p 0 6 2}note: {bf:mv_bluecollar} omitted because of collinearity.{p_end}
{p 0 6 2}note: {bf:mv_serviceworker} omitted because of collinearity.{p_end}
{p 0 6 2}note: {bf:4.country} omitted because of collinearity.{p_end}
{p 0 6 2}note: {bf:7.country} omitted because of collinearity.{p_end}
{p 0 6 2}note: {bf:9.country} omitted because of collinearity.{p_end}
{p 0 6 2}note: {bf:11.country} omitted because of collinearity.{p_end}
{p 0 6 2}note: {bf:12.country} omitted because of collinearity.{p_end}

Linear regression                               Number of obs     = {res}    22,541
                                                {txt}F(44, 22496)      =  {res}    77.86
                                                {txt}Prob > F          = {res}    0.0000
                                                {txt}R-squared         = {res}    0.1199
                                                {txt}Root MSE          =    {res} .29493

{txt}{hline 24}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 25}{c |}{col 37}    Robust
{col 1}             satis_head{col 25}{c |} Coefficient{col 37}  std. err.{col 49}      t{col 57}   P>|t|{col 65}     [95% con{col 78}f. interval]
{hline 24}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 16}healthc {c |}{col 25}{res}{space 2} .0140949{col 37}{space 2} .0049573{col 48}{space 1}    2.84{col 57}{space 3}0.004{col 65}{space 4} .0043783{col 78}{space 3} .0238116
{txt}{space 18}econc {c |}{col 25}{res}{space 2} .0082751{col 37}{space 2}  .004977{col 48}{space 1}    1.66{col 57}{space 3}0.096{col 65}{space 4}-.0014802{col 78}{space 3} .0180304
{txt}{space 15}thirties {c |}{col 25}{res}{space 2}-.0134234{col 37}{space 2} .0067484{col 48}{space 1}   -1.99{col 57}{space 3}0.047{col 65}{space 4}-.0266507{col 78}{space 3}-.0001961
{txt}{space 15}fourties {c |}{col 25}{res}{space 2}-.0316119{col 37}{space 2} .0067686{col 48}{space 1}   -4.67{col 57}{space 3}0.000{col 65}{space 4}-.0448789{col 78}{space 3}-.0183448
{txt}{space 16}fifties {c |}{col 25}{res}{space 2}-.0337981{col 37}{space 2} .0070997{col 48}{space 1}   -4.76{col 57}{space 3}0.000{col 65}{space 4} -.047714{col 78}{space 3}-.0198822
{txt}{space 16}sixties {c |}{col 25}{res}{space 2}-.0356929{col 37}{space 2} .0071289{col 48}{space 1}   -5.01{col 57}{space 3}0.000{col 65}{space 4}-.0496661{col 78}{space 3}-.0217197
{txt}{space 14}seventies {c |}{col 25}{res}{space 2}-.0138311{col 37}{space 2}  .008694{col 48}{space 1}   -1.59{col 57}{space 3}0.112{col 65}{space 4}-.0308718{col 78}{space 3} .0032097
{txt}{space 8}income2quartile {c |}{col 25}{res}{space 2}  .017226{col 37}{space 2} .0058043{col 48}{space 1}    2.97{col 57}{space 3}0.003{col 65}{space 4} .0058491{col 78}{space 3} .0286029
{txt}{space 8}income3quartile {c |}{col 25}{res}{space 2} .0228922{col 37}{space 2}  .005991{col 48}{space 1}    3.82{col 57}{space 3}0.000{col 65}{space 4} .0111495{col 78}{space 3} .0346349
{txt}{space 8}income4quartile {c |}{col 25}{res}{space 2} .0412933{col 37}{space 2} .0061355{col 48}{space 1}    6.73{col 57}{space 3}0.000{col 65}{space 4} .0292674{col 78}{space 3} .0533193
{txt}{space 9}incomenoanswer {c |}{col 25}{res}{space 2}-.0097535{col 37}{space 2} .0093956{col 48}{space 1}   -1.04{col 57}{space 3}0.299{col 65}{space 4}-.0281694{col 78}{space 3} .0086625
{txt}{space 17}female {c |}{col 25}{res}{space 2} .0108392{col 37}{space 2} .0040344{col 48}{space 1}    2.69{col 57}{space 3}0.007{col 65}{space 4} .0029315{col 78}{space 3} .0187469
{txt}{space 13}highschool {c |}{col 25}{res}{space 2} .0091496{col 37}{space 2} .0065538{col 48}{space 1}    1.40{col 57}{space 3}0.163{col 65}{space 4}-.0036963{col 78}{space 3} .0219955
{txt}{space 16}college {c |}{col 25}{res}{space 2} .0024371{col 37}{space 2} .0061075{col 48}{space 1}    0.40{col 57}{space 3}0.690{col 65}{space 4}-.0095341{col 78}{space 3} .0144083
{txt}{space 13}noreligion {c |}{col 25}{res}{space 2}-.0312582{col 37}{space 2} .0084083{col 48}{space 1}   -3.72{col 57}{space 3}0.000{col 65}{space 4}-.0477392{col 78}{space 3}-.0147773
{txt}{space 3}christiannotcatholic {c |}{col 25}{res}{space 2} .0639444{col 37}{space 2} .0098316{col 48}{space 1}    6.50{col 57}{space 3}0.000{col 65}{space 4} .0446737{col 78}{space 3} .0832151
{txt}{space 15}catholic {c |}{col 25}{res}{space 2} .0213995{col 37}{space 2} .0086221{col 48}{space 1}    2.48{col 57}{space 3}0.013{col 65}{space 4} .0044995{col 78}{space 3} .0382995
{txt}{space 9}fulltimeworker {c |}{col 25}{res}{space 2}-.3124523{col 37}{space 2} .0097908{col 48}{space 1}  -31.91{col 57}{space 3}0.000{col 65}{space 4} -.331643{col 78}{space 3}-.2932617
{txt}{space 9}parttimeworker {c |}{col 25}{res}{space 2}  -.30571{col 37}{space 2} .0248774{col 48}{space 1}  -12.29{col 57}{space 3}0.000{col 65}{space 4}-.3544714{col 78}{space 3}-.2569485
{txt}{space 13}unemployed {c |}{col 25}{res}{space 2}-.3458249{col 37}{space 2} .0152697{col 48}{space 1}  -22.65{col 57}{space 3}0.000{col 65}{space 4}-.3757547{col 78}{space 3}-.3158952
{txt}{space 11}selfemployed {c |}{col 25}{res}{space 2} -.369366{col 37}{space 2} .0386102{col 48}{space 1}   -9.57{col 57}{space 3}0.000{col 65}{space 4}-.4450446{col 78}{space 3}-.2936874
{txt}{space 16}outofLF {c |}{col 25}{res}{space 2}-.3243856{col 37}{space 2} .0105845{col 48}{space 1}  -30.65{col 57}{space 3}0.000{col 65}{space 4}-.3451319{col 78}{space 3}-.3036392
{txt}{space 13}goodhealth {c |}{col 25}{res}{space 2} .0501586{col 37}{space 2} .0042206{col 48}{space 1}   11.88{col 57}{space 3}0.000{col 65}{space 4} .0418859{col 78}{space 3} .0584312
{txt}{space 18}white {c |}{col 25}{res}{space 2} .1321865{col 37}{space 2} .0429327{col 48}{space 1}    3.08{col 57}{space 3}0.002{col 65}{space 4} .0480354{col 78}{space 3} .2163376
{txt}{space 18}black {c |}{col 25}{res}{space 2} .0116757{col 37}{space 2} .0478136{col 48}{space 1}    0.24{col 57}{space 3}0.807{col 65}{space 4}-.0820423{col 78}{space 3} .1053936
{txt}{space 17}latino {c |}{col 25}{res}{space 2} .0365082{col 37}{space 2} .0530087{col 48}{space 1}    0.69{col 57}{space 3}0.491{col 65}{space 4}-.0673926{col 78}{space 3}  .140409
{txt}{space 18}asian {c |}{col 25}{res}{space 2} .0734831{col 37}{space 2} .0531508{col 48}{space 1}    1.38{col 57}{space 3}0.167{col 65}{space 4}-.0306962{col 78}{space 3} .1776624
{txt}{space 12}whitecollar {c |}{col 25}{res}{space 2}-.0171754{col 37}{space 2} .0106565{col 48}{space 1}   -1.61{col 57}{space 3}0.107{col 65}{space 4}-.0380628{col 78}{space 3}  .003712
{txt}{space 13}bluecollar {c |}{col 25}{res}{space 2}-.0235775{col 37}{space 2} .0109768{col 48}{space 1}   -2.15{col 57}{space 3}0.032{col 65}{space 4}-.0450928{col 78}{space 3}-.0020622
{txt}{space 10}serviceworker {c |}{col 25}{res}{space 2}-.0098776{col 37}{space 2} .0087807{col 48}{space 1}   -1.12{col 57}{space 3}0.261{col 65}{space 4}-.0270884{col 78}{space 3} .0073331
{txt}{space 12}mv_thirties {c |}{col 25}{res}{space 2}        0{col 37}{txt}  (omitted)
{space 12}mv_fourties {c |}{col 25}{res}{space 2}        0{col 37}{txt}  (omitted)
{space 13}mv_fifties {c |}{col 25}{res}{space 2}        0{col 37}{txt}  (omitted)
{space 13}mv_sixties {c |}{col 25}{res}{space 2}        0{col 37}{txt}  (omitted)
{space 11}mv_seventies {c |}{col 25}{res}{space 2}        0{col 37}{txt}  (omitted)
{space 5}mv_income2quartile {c |}{col 25}{res}{space 2}        0{col 37}{txt}  (omitted)
{space 5}mv_income3quartile {c |}{col 25}{res}{space 2}        0{col 37}{txt}  (omitted)
{space 5}mv_income4quartile {c |}{col 25}{res}{space 2}        0{col 37}{txt}  (omitted)
{space 6}mv_incomenoanswer {c |}{col 25}{res}{space 2} -.074132{col 37}{space 2}  .012295{col 48}{space 1}   -6.03{col 57}{space 3}0.000{col 65}{space 4}-.0982311{col 78}{space 3}-.0500329
{txt}{space 14}mv_female {c |}{col 25}{res}{space 2}        0{col 37}{txt}  (omitted)
{space 10}mv_highschool {c |}{col 25}{res}{space 2}-.0108424{col 37}{space 2}  .015756{col 48}{space 1}   -0.69{col 57}{space 3}0.491{col 65}{space 4}-.0417253{col 78}{space 3} .0200405
{txt}{space 13}mv_college {c |}{col 25}{res}{space 2}        0{col 37}{txt}  (omitted)
{space 10}mv_noreligion {c |}{col 25}{res}{space 2} .0039674{col 37}{space 2} .0289241{col 48}{space 1}    0.14{col 57}{space 3}0.891{col 65}{space 4}-.0527258{col 78}{space 3} .0606606
{txt}mv_christiannotcatholic {c |}{col 25}{res}{space 2}        0{col 37}{txt}  (omitted)
{space 12}mv_catholic {c |}{col 25}{res}{space 2}        0{col 37}{txt}  (omitted)
{space 6}mv_fulltimeworker {c |}{col 25}{res}{space 2}        0{col 37}{txt}  (omitted)
{space 6}mv_parttimeworker {c |}{col 25}{res}{space 2}-.2887288{col 37}{space 2} .0196855{col 48}{space 1}  -14.67{col 57}{space 3}0.000{col 65}{space 4}-.3273137{col 78}{space 3} -.250144
{txt}{space 10}mv_unemployed {c |}{col 25}{res}{space 2}        0{col 37}{txt}  (omitted)
{space 8}mv_selfemployed {c |}{col 25}{res}{space 2} .2965258{col 37}{space 2} .0213009{col 48}{space 1}   13.92{col 57}{space 3}0.000{col 65}{space 4} .2547746{col 78}{space 3} .3382771
{txt}{space 13}mv_outofLF {c |}{col 25}{res}{space 2}        0{col 37}{txt}  (omitted)
{space 10}mv_goodhealth {c |}{col 25}{res}{space 2} .1758223{col 37}{space 2}  .072304{col 48}{space 1}    2.43{col 57}{space 3}0.015{col 65}{space 4} .0341015{col 78}{space 3} .3175432
{txt}{space 15}mv_white {c |}{col 25}{res}{space 2}  .381458{col 37}{space 2} .0426313{col 48}{space 1}    8.95{col 57}{space 3}0.000{col 65}{space 4} .2978977{col 78}{space 3} .4650183
{txt}{space 15}mv_black {c |}{col 25}{res}{space 2}        0{col 37}{txt}  (omitted)
{space 14}mv_latino {c |}{col 25}{res}{space 2}        0{col 37}{txt}  (omitted)
{space 15}mv_asian {c |}{col 25}{res}{space 2}        0{col 37}{txt}  (omitted)
{space 9}mv_whitecollar {c |}{col 25}{res}{space 2} .3037019{col 37}{space 2} .0205883{col 48}{space 1}   14.75{col 57}{space 3}0.000{col 65}{space 4} .2633473{col 78}{space 3} .3440564
{txt}{space 10}mv_bluecollar {c |}{col 25}{res}{space 2}        0{col 37}{txt}  (omitted)
{space 7}mv_serviceworker {c |}{col 25}{res}{space 2}        0{col 37}{txt}  (omitted)
{space 23} {c |}
{space 16}country {c |}
{space 15}Austria  {c |}{col 25}{res}{space 2} .5161327{col 37}{space 2} .0473284{col 48}{space 1}   10.91{col 57}{space 3}0.000{col 65}{space 4} .4233657{col 78}{space 3} .6088997
{txt}{space 16}Brazil  {c |}{col 25}{res}{space 2}-.0159355{col 37}{space 2} .0213515{col 48}{space 1}   -0.75{col 57}{space 3}0.455{col 65}{space 4} -.057786{col 78}{space 3} .0259149
{txt}{space 16}France  {c |}{col 25}{res}{space 2}        0{col 37}{txt}  (omitted)
{space 15}Germany  {c |}{col 25}{res}{space 2} .5549959{col 37}{space 2} .0467743{col 48}{space 1}   11.87{col 57}{space 3}0.000{col 65}{space 4}  .463315{col 78}{space 3} .6466768
{txt}{space 17}Italy  {c |}{col 25}{res}{space 2} .4912957{col 37}{space 2}  .054765{col 48}{space 1}    8.97{col 57}{space 3}0.000{col 65}{space 4} .3839526{col 78}{space 3} .5986388
{txt}{space 11}New_Zealand  {c |}{col 25}{res}{space 2}        0{col 37}{txt}  (omitted)
{space 16}Poland  {c |}{col 25}{res}{space 2}-.0342309{col 37}{space 2} .0214604{col 48}{space 1}   -1.60{col 57}{space 3}0.111{col 65}{space 4}-.0762947{col 78}{space 3} .0078329
{txt}{space 17}Spain  {c |}{col 25}{res}{space 2}        0{col 37}{txt}  (omitted)
{space 16}Sweden  {c |}{col 25}{res}{space 2} .0588046{col 37}{space 2} .0204579{col 48}{space 1}    2.87{col 57}{space 3}0.004{col 65}{space 4} .0187057{col 78}{space 3} .0989034
{txt}{space 20}UK  {c |}{col 25}{res}{space 2}        0{col 37}{txt}  (omitted)
{space 20}US  {c |}{col 25}{res}{space 2}        0{col 37}{txt}  (omitted)
{space 23} {c |}
{space 18}_cons {c |}{col 25}{res}{space 2}-.0201202{col 37}{space 2} .0486856{col 48}{space 1}   -0.41{col 57}{space 3}0.679{col 65}{space 4}-.1155473{col 78}{space 3} .0753069
{txt}{hline 24}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{res}{txt}({res}est4{txt} stored)

{com}. estadd local CFE "X", replace

{txt}added macro:
                e(CFE) : "{res:X}"

{com}. estadd local Controls "X", replace

{txt}added macro:
           e(Controls) : "{res:X}"

{com}. test  healthc econc

{p 0 7}{space 1}{text:( 1)}{space 1} {res}healthc = 0{p_end}
{p 0 7}{space 1}{text:( 2)}{space 1} econc = 0{p_end}

{txt}       F(  2, 22496) ={res}    5.45
{txt}{col 13}Prob > F ={res}    0.0043
{txt}
{com}. estadd scalar F = r(F), replace

{txt}added scalar:
                  e(F) =  {res}5.4492125
{txt}
{com}. 
. eststo: reg satis_head treatc, robust

{txt}Linear regression                               Number of obs     = {res}    22,541
                                                {txt}F(1, 22539)       =  {res}     8.60
                                                {txt}Prob > F          = {res}    0.0034
                                                {txt}R-squared         = {res}    0.0004
                                                {txt}Root MSE          =    {res} .31402

{txt}{hline 13}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 14}{c |}{col 26}    Robust
{col 1}  satis_head{col 14}{c |} Coefficient{col 26}  std. err.{col 38}      t{col 46}   P>|t|{col 54}     [95% con{col 67}f. interval]
{hline 13}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 6}treatc {c |}{col 14}{res}{space 2} .0109407{col 26}{space 2} .0037318{col 37}{space 1}    2.93{col 46}{space 3}0.003{col 54}{space 4} .0036262{col 67}{space 3} .0182553
{txt}{space 7}_cons {c |}{col 14}{res}{space 2} .4582076{col 26}{space 2} .0020915{col 37}{space 1}  219.08{col 46}{space 3}0.000{col 54}{space 4} .4541081{col 67}{space 3} .4623071
{txt}{hline 13}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{res}{txt}({res}est5{txt} stored)

{com}. estadd scalar F = e(F), replace

{txt}added scalar:
                  e(F) =  {res}8.5952917
{txt}
{com}. 
. eststo: reg satis_head treatc $controls $mv_controls i.country, robust
{txt}{p 0 6 2}note: {bf:mv_thirties} omitted because of collinearity.{p_end}
{p 0 6 2}note: {bf:mv_fourties} omitted because of collinearity.{p_end}
{p 0 6 2}note: {bf:mv_fifties} omitted because of collinearity.{p_end}
{p 0 6 2}note: {bf:mv_sixties} omitted because of collinearity.{p_end}
{p 0 6 2}note: {bf:mv_seventies} omitted because of collinearity.{p_end}
{p 0 6 2}note: {bf:mv_income2quartile} omitted because of collinearity.{p_end}
{p 0 6 2}note: {bf:mv_income3quartile} omitted because of collinearity.{p_end}
{p 0 6 2}note: {bf:mv_income4quartile} omitted because of collinearity.{p_end}
{p 0 6 2}note: {bf:mv_female} omitted because of collinearity.{p_end}
{p 0 6 2}note: {bf:mv_college} omitted because of collinearity.{p_end}
{p 0 6 2}note: {bf:mv_christiannotcatholic} omitted because of collinearity.{p_end}
{p 0 6 2}note: {bf:mv_catholic} omitted because of collinearity.{p_end}
{p 0 6 2}note: {bf:mv_fulltimeworker} omitted because of collinearity.{p_end}
{p 0 6 2}note: {bf:mv_unemployed} omitted because of collinearity.{p_end}
{p 0 6 2}note: {bf:mv_outofLF} omitted because of collinearity.{p_end}
{p 0 6 2}note: {bf:mv_black} omitted because of collinearity.{p_end}
{p 0 6 2}note: {bf:mv_latino} omitted because of collinearity.{p_end}
{p 0 6 2}note: {bf:mv_asian} omitted because of collinearity.{p_end}
{p 0 6 2}note: {bf:mv_bluecollar} omitted because of collinearity.{p_end}
{p 0 6 2}note: {bf:mv_serviceworker} omitted because of collinearity.{p_end}
{p 0 6 2}note: {bf:4.country} omitted because of collinearity.{p_end}
{p 0 6 2}note: {bf:7.country} omitted because of collinearity.{p_end}
{p 0 6 2}note: {bf:9.country} omitted because of collinearity.{p_end}
{p 0 6 2}note: {bf:11.country} omitted because of collinearity.{p_end}
{p 0 6 2}note: {bf:12.country} omitted because of collinearity.{p_end}

Linear regression                               Number of obs     = {res}    22,541
                                                {txt}F(43, 22497)      =  {res}    79.68
                                                {txt}Prob > F          = {res}    0.0000
                                                {txt}R-squared         = {res}    0.1199
                                                {txt}Root MSE          =    {res} .29493

{txt}{hline 24}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 25}{c |}{col 37}    Robust
{col 1}             satis_head{col 25}{c |} Coefficient{col 37}  std. err.{col 49}      t{col 57}   P>|t|{col 65}     [95% con{col 78}f. interval]
{hline 24}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 17}treatc {c |}{col 25}{res}{space 2}  .011192{col 37}{space 2}  .003503{col 48}{space 1}    3.19{col 57}{space 3}0.001{col 65}{space 4} .0043258{col 78}{space 3} .0180582
{txt}{space 15}thirties {c |}{col 25}{res}{space 2}-.0134993{col 37}{space 2} .0067478{col 48}{space 1}   -2.00{col 57}{space 3}0.045{col 65}{space 4}-.0267255{col 78}{space 3}-.0002731
{txt}{space 15}fourties {c |}{col 25}{res}{space 2}-.0317209{col 37}{space 2} .0067695{col 48}{space 1}   -4.69{col 57}{space 3}0.000{col 65}{space 4}-.0449895{col 78}{space 3}-.0184522
{txt}{space 16}fifties {c |}{col 25}{res}{space 2}-.0338765{col 37}{space 2} .0071004{col 48}{space 1}   -4.77{col 57}{space 3}0.000{col 65}{space 4}-.0477938{col 78}{space 3}-.0199592
{txt}{space 16}sixties {c |}{col 25}{res}{space 2}-.0357273{col 37}{space 2} .0071288{col 48}{space 1}   -5.01{col 57}{space 3}0.000{col 65}{space 4}-.0497003{col 78}{space 3}-.0217543
{txt}{space 14}seventies {c |}{col 25}{res}{space 2}-.0139135{col 37}{space 2} .0086924{col 48}{space 1}   -1.60{col 57}{space 3}0.109{col 65}{space 4}-.0309511{col 78}{space 3} .0031241
{txt}{space 8}income2quartile {c |}{col 25}{res}{space 2} .0171702{col 37}{space 2} .0058044{col 48}{space 1}    2.96{col 57}{space 3}0.003{col 65}{space 4} .0057933{col 78}{space 3} .0285472
{txt}{space 8}income3quartile {c |}{col 25}{res}{space 2} .0228811{col 37}{space 2} .0059909{col 48}{space 1}    3.82{col 57}{space 3}0.000{col 65}{space 4} .0111386{col 78}{space 3} .0346237
{txt}{space 8}income4quartile {c |}{col 25}{res}{space 2}  .041313{col 37}{space 2} .0061357{col 48}{space 1}    6.73{col 57}{space 3}0.000{col 65}{space 4} .0292866{col 78}{space 3} .0533393
{txt}{space 9}incomenoanswer {c |}{col 25}{res}{space 2} -.009791{col 37}{space 2} .0093952{col 48}{space 1}   -1.04{col 57}{space 3}0.297{col 65}{space 4}-.0282063{col 78}{space 3} .0086242
{txt}{space 17}female {c |}{col 25}{res}{space 2} .0108716{col 37}{space 2} .0040345{col 48}{space 1}    2.69{col 57}{space 3}0.007{col 65}{space 4} .0029638{col 78}{space 3} .0187794
{txt}{space 13}highschool {c |}{col 25}{res}{space 2} .0091119{col 37}{space 2} .0065543{col 48}{space 1}    1.39{col 57}{space 3}0.164{col 65}{space 4} -.003735{col 78}{space 3} .0219589
{txt}{space 16}college {c |}{col 25}{res}{space 2} .0023838{col 37}{space 2} .0061071{col 48}{space 1}    0.39{col 57}{space 3}0.696{col 65}{space 4}-.0095866{col 78}{space 3} .0143542
{txt}{space 13}noreligion {c |}{col 25}{res}{space 2}-.0312597{col 37}{space 2} .0084078{col 48}{space 1}   -3.72{col 57}{space 3}0.000{col 65}{space 4}-.0477396{col 78}{space 3}-.0147797
{txt}{space 3}christiannotcatholic {c |}{col 25}{res}{space 2} .0638749{col 37}{space 2} .0098299{col 48}{space 1}    6.50{col 57}{space 3}0.000{col 65}{space 4} .0446075{col 78}{space 3} .0831423
{txt}{space 15}catholic {c |}{col 25}{res}{space 2} .0213565{col 37}{space 2} .0086216{col 48}{space 1}    2.48{col 57}{space 3}0.013{col 65}{space 4} .0044576{col 78}{space 3} .0382554
{txt}{space 9}fulltimeworker {c |}{col 25}{res}{space 2}-.3124377{col 37}{space 2} .0097883{col 48}{space 1}  -31.92{col 57}{space 3}0.000{col 65}{space 4}-.3316234{col 78}{space 3}-.2932519
{txt}{space 9}parttimeworker {c |}{col 25}{res}{space 2}-.3059022{col 37}{space 2}  .024864{col 48}{space 1}  -12.30{col 57}{space 3}0.000{col 65}{space 4}-.3546373{col 78}{space 3}-.2571671
{txt}{space 13}unemployed {c |}{col 25}{res}{space 2}-.3458701{col 37}{space 2} .0152678{col 48}{space 1}  -22.65{col 57}{space 3}0.000{col 65}{space 4}-.3757961{col 78}{space 3}-.3159441
{txt}{space 11}selfemployed {c |}{col 25}{res}{space 2}-.3694157{col 37}{space 2} .0386366{col 48}{space 1}   -9.56{col 57}{space 3}0.000{col 65}{space 4}-.4451461{col 78}{space 3}-.2936853
{txt}{space 16}outofLF {c |}{col 25}{res}{space 2}-.3244064{col 37}{space 2} .0105822{col 48}{space 1}  -30.66{col 57}{space 3}0.000{col 65}{space 4}-.3451483{col 78}{space 3}-.3036645
{txt}{space 13}goodhealth {c |}{col 25}{res}{space 2} .0501812{col 37}{space 2} .0042203{col 48}{space 1}   11.89{col 57}{space 3}0.000{col 65}{space 4} .0419092{col 78}{space 3} .0584532
{txt}{space 18}white {c |}{col 25}{res}{space 2}  .132114{col 37}{space 2} .0429957{col 48}{space 1}    3.07{col 57}{space 3}0.002{col 65}{space 4} .0478395{col 78}{space 3} .2163884
{txt}{space 18}black {c |}{col 25}{res}{space 2}  .011278{col 37}{space 2} .0478744{col 48}{space 1}    0.24{col 57}{space 3}0.814{col 65}{space 4}-.0825592{col 78}{space 3} .1051151
{txt}{space 17}latino {c |}{col 25}{res}{space 2}  .036269{col 37}{space 2} .0530732{col 48}{space 1}    0.68{col 57}{space 3}0.494{col 65}{space 4}-.0677581{col 78}{space 3} .1402962
{txt}{space 18}asian {c |}{col 25}{res}{space 2} .0735465{col 37}{space 2} .0531933{col 48}{space 1}    1.38{col 57}{space 3}0.167{col 65}{space 4}-.0307161{col 78}{space 3} .1778092
{txt}{space 12}whitecollar {c |}{col 25}{res}{space 2}-.0171806{col 37}{space 2} .0106568{col 48}{space 1}   -1.61{col 57}{space 3}0.107{col 65}{space 4}-.0380686{col 78}{space 3} .0037075
{txt}{space 13}bluecollar {c |}{col 25}{res}{space 2} -.023664{col 37}{space 2} .0109767{col 48}{space 1}   -2.16{col 57}{space 3}0.031{col 65}{space 4} -.045179{col 78}{space 3}-.0021489
{txt}{space 10}serviceworker {c |}{col 25}{res}{space 2}-.0098717{col 37}{space 2} .0087806{col 48}{space 1}   -1.12{col 57}{space 3}0.261{col 65}{space 4}-.0270823{col 78}{space 3} .0073389
{txt}{space 12}mv_thirties {c |}{col 25}{res}{space 2}        0{col 37}{txt}  (omitted)
{space 12}mv_fourties {c |}{col 25}{res}{space 2}        0{col 37}{txt}  (omitted)
{space 13}mv_fifties {c |}{col 25}{res}{space 2}        0{col 37}{txt}  (omitted)
{space 13}mv_sixties {c |}{col 25}{res}{space 2}        0{col 37}{txt}  (omitted)
{space 11}mv_seventies {c |}{col 25}{res}{space 2}        0{col 37}{txt}  (omitted)
{space 5}mv_income2quartile {c |}{col 25}{res}{space 2}        0{col 37}{txt}  (omitted)
{space 5}mv_income3quartile {c |}{col 25}{res}{space 2}        0{col 37}{txt}  (omitted)
{space 5}mv_income4quartile {c |}{col 25}{res}{space 2}        0{col 37}{txt}  (omitted)
{space 6}mv_incomenoanswer {c |}{col 25}{res}{space 2}-.0741393{col 37}{space 2} .0122925{col 48}{space 1}   -6.03{col 57}{space 3}0.000{col 65}{space 4}-.0982334{col 78}{space 3}-.0500451
{txt}{space 14}mv_female {c |}{col 25}{res}{space 2}        0{col 37}{txt}  (omitted)
{space 10}mv_highschool {c |}{col 25}{res}{space 2}-.0108976{col 37}{space 2}  .015754{col 48}{space 1}   -0.69{col 57}{space 3}0.489{col 65}{space 4}-.0417765{col 78}{space 3} .0199813
{txt}{space 13}mv_college {c |}{col 25}{res}{space 2}        0{col 37}{txt}  (omitted)
{space 10}mv_noreligion {c |}{col 25}{res}{space 2} .0044899{col 37}{space 2} .0289229{col 48}{space 1}    0.16{col 57}{space 3}0.877{col 65}{space 4} -.052201{col 78}{space 3} .0611809
{txt}mv_christiannotcatholic {c |}{col 25}{res}{space 2}        0{col 37}{txt}  (omitted)
{space 12}mv_catholic {c |}{col 25}{res}{space 2}        0{col 37}{txt}  (omitted)
{space 6}mv_fulltimeworker {c |}{col 25}{res}{space 2}        0{col 37}{txt}  (omitted)
{space 6}mv_parttimeworker {c |}{col 25}{res}{space 2}-.2888553{col 37}{space 2} .0196819{col 48}{space 1}  -14.68{col 57}{space 3}0.000{col 65}{space 4}-.3274332{col 78}{space 3}-.2502774
{txt}{space 10}mv_unemployed {c |}{col 25}{res}{space 2}        0{col 37}{txt}  (omitted)
{space 8}mv_selfemployed {c |}{col 25}{res}{space 2} .2966281{col 37}{space 2} .0212971{col 48}{space 1}   13.93{col 57}{space 3}0.000{col 65}{space 4} .2548842{col 78}{space 3} .3383719
{txt}{space 13}mv_outofLF {c |}{col 25}{res}{space 2}        0{col 37}{txt}  (omitted)
{space 10}mv_goodhealth {c |}{col 25}{res}{space 2} .1753029{col 37}{space 2}  .072225{col 48}{space 1}    2.43{col 57}{space 3}0.015{col 65}{space 4} .0337368{col 78}{space 3}  .316869
{txt}{space 15}mv_white {c |}{col 25}{res}{space 2} .3813067{col 37}{space 2} .0426955{col 48}{space 1}    8.93{col 57}{space 3}0.000{col 65}{space 4} .2976205{col 78}{space 3} .4649929
{txt}{space 15}mv_black {c |}{col 25}{res}{space 2}        0{col 37}{txt}  (omitted)
{space 14}mv_latino {c |}{col 25}{res}{space 2}        0{col 37}{txt}  (omitted)
{space 15}mv_asian {c |}{col 25}{res}{space 2}        0{col 37}{txt}  (omitted)
{space 9}mv_whitecollar {c |}{col 25}{res}{space 2} .3036642{col 37}{space 2} .0205863{col 48}{space 1}   14.75{col 57}{space 3}0.000{col 65}{space 4} .2633135{col 78}{space 3} .3440148
{txt}{space 10}mv_bluecollar {c |}{col 25}{res}{space 2}        0{col 37}{txt}  (omitted)
{space 7}mv_serviceworker {c |}{col 25}{res}{space 2}        0{col 37}{txt}  (omitted)
{space 23} {c |}
{space 16}country {c |}
{space 15}Austria  {c |}{col 25}{res}{space 2} .5159672{col 37}{space 2} .0473876{col 48}{space 1}   10.89{col 57}{space 3}0.000{col 65}{space 4} .4230843{col 78}{space 3} .6088501
{txt}{space 16}Brazil  {c |}{col 25}{res}{space 2} -.015949{col 37}{space 2} .0213485{col 48}{space 1}   -0.75{col 57}{space 3}0.455{col 65}{space 4}-.0577936{col 78}{space 3} .0258957
{txt}{space 16}France  {c |}{col 25}{res}{space 2}        0{col 37}{txt}  (omitted)
{space 15}Germany  {c |}{col 25}{res}{space 2} .5548393{col 37}{space 2} .0468331{col 48}{space 1}   11.85{col 57}{space 3}0.000{col 65}{space 4} .4630431{col 78}{space 3} .6466355
{txt}{space 17}Italy  {c |}{col 25}{res}{space 2} .4905779{col 37}{space 2} .0548231{col 48}{space 1}    8.95{col 57}{space 3}0.000{col 65}{space 4} .3831208{col 78}{space 3} .5980349
{txt}{space 11}New_Zealand  {c |}{col 25}{res}{space 2}        0{col 37}{txt}  (omitted)
{space 16}Poland  {c |}{col 25}{res}{space 2}-.0342285{col 37}{space 2} .0214599{col 48}{space 1}   -1.60{col 57}{space 3}0.111{col 65}{space 4}-.0762913{col 78}{space 3} .0078343
{txt}{space 17}Spain  {c |}{col 25}{res}{space 2}        0{col 37}{txt}  (omitted)
{space 16}Sweden  {c |}{col 25}{res}{space 2} .0587884{col 37}{space 2} .0204569{col 48}{space 1}    2.87{col 57}{space 3}0.004{col 65}{space 4} .0186914{col 78}{space 3} .0988853
{txt}{space 20}UK  {c |}{col 25}{res}{space 2}        0{col 37}{txt}  (omitted)
{space 20}US  {c |}{col 25}{res}{space 2}        0{col 37}{txt}  (omitted)
{space 23} {c |}
{space 18}_cons {c |}{col 25}{res}{space 2}-.0086164{col 37}{space 2} .0486162{col 48}{space 1}   -0.18{col 57}{space 3}0.859{col 65}{space 4}-.1039076{col 78}{space 3} .0866748
{txt}{hline 24}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{res}{txt}({res}est6{txt} stored)

{com}. estadd local CFE "X", replace

{txt}added macro:
                e(CFE) : "{res:X}"

{com}. estadd local Controls "X", replace

{txt}added macro:
           e(Controls) : "{res:X}"

{com}. test  treatc

{p 0 7}{space 1}{text:( 1)}{space 1} {res}treatc = 0{p_end}

{txt}       F(  1, 22497) ={res}   10.21
{txt}{col 13}Prob > F ={res}    0.0014
{txt}
{com}. estadd scalar F = r(F), replace

{txt}added scalar:
                  e(F) =  {res}10.207647
{txt}
{com}. 
. esttab using "3_output/2_OA/Tables/TableE6.tex", depvar keep(TH* healthc econc treatc) cons ///
>         label replace order(_cons healthc econc treatc TH1_TE1 TH1_TE2 TH1_TE3 TH1_TE4 TH2_TE1 TH2_TE2 TH2_TE3 TH2_TE4 TH3_TE1 TH3_TE2 TH3_TE3 TH3_TE4 TH4_TE1 TH4_TE2 TH4_TE3) ///
>         cells(b(star fmt(%15.3fc)) se(par fmt(%15.3fc))) interaction(" $\times$ ") ///
>         star(* 0.10 ** 0.05 *** 0.01) ///
>         stats(Controls CFE N  F , layout(@ @ @ @  ) fmt(%15s %15s %15.0fc %9.3f) labels("Controls" "Country FE" Observations  "F-statistic")) ///
>         collabels(none) mlabels(none) ///
>         mgroups("Satisfaction with the head of government" , pattern(1 0 0) ///
>         prefix(\multicolumn{c -(}@span{c )-}{c -(}c{c )-}{c -(}) suffix({c )-}) span erepeat(\cmidrule(lr){c -(}@span{c )-}))
{res}{txt}(output written to {browse  `"3_output/2_OA/Tables/TableE6.tex"'})

{com}. 
. **# Table E.7. OLS estimates of the correlation between satisfaction with the head of government and attitudes on democracy.
. 
. eststo clear
{txt}
{com}. eststo: reg satis_dem satis_head,  robust 

{txt}Linear regression                               Number of obs     = {res}    22,541
                                                {txt}F(1, 22539)       =  {res} 12475.63
                                                {txt}Prob > F          = {res}    0.0000
                                                {txt}R-squared         = {res}    0.4004
                                                {txt}Root MSE          =    {res} .20861

{txt}{hline 13}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 14}{c |}{col 26}    Robust
{col 1}   satis_dem{col 14}{c |} Coefficient{col 26}  std. err.{col 38}      t{col 46}   P>|t|{col 54}     [95% con{col 67}f. interval]
{hline 13}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 2}satis_head {c |}{col 14}{res}{space 2} .5427112{col 26}{space 2} .0048589{col 37}{space 1}  111.69{col 46}{space 3}0.000{col 54}{space 4} .5331874{col 67}{space 3} .5522349
{txt}{space 7}_cons {c |}{col 14}{res}{space 2} .2512253{col 26}{space 2} .0027542{col 37}{space 1}   91.22{col 46}{space 3}0.000{col 54}{space 4} .2458269{col 67}{space 3} .2566238
{txt}{hline 13}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{res}{txt}({res}est1{txt} stored)

{com}. get_mean

{txt}added scalar:
                 e(av) =  {res}.5
{txt}
{com}. 
. eststo: reg satis_dem satis_head  $controls $mv_controls i.country,  robust 
{txt}{p 0 6 2}note: {bf:mv_thirties} omitted because of collinearity.{p_end}
{p 0 6 2}note: {bf:mv_fourties} omitted because of collinearity.{p_end}
{p 0 6 2}note: {bf:mv_fifties} omitted because of collinearity.{p_end}
{p 0 6 2}note: {bf:mv_sixties} omitted because of collinearity.{p_end}
{p 0 6 2}note: {bf:mv_seventies} omitted because of collinearity.{p_end}
{p 0 6 2}note: {bf:mv_income2quartile} omitted because of collinearity.{p_end}
{p 0 6 2}note: {bf:mv_income3quartile} omitted because of collinearity.{p_end}
{p 0 6 2}note: {bf:mv_income4quartile} omitted because of collinearity.{p_end}
{p 0 6 2}note: {bf:mv_female} omitted because of collinearity.{p_end}
{p 0 6 2}note: {bf:mv_college} omitted because of collinearity.{p_end}
{p 0 6 2}note: {bf:mv_christiannotcatholic} omitted because of collinearity.{p_end}
{p 0 6 2}note: {bf:mv_catholic} omitted because of collinearity.{p_end}
{p 0 6 2}note: {bf:mv_fulltimeworker} omitted because of collinearity.{p_end}
{p 0 6 2}note: {bf:mv_unemployed} omitted because of collinearity.{p_end}
{p 0 6 2}note: {bf:mv_outofLF} omitted because of collinearity.{p_end}
{p 0 6 2}note: {bf:mv_black} omitted because of collinearity.{p_end}
{p 0 6 2}note: {bf:mv_latino} omitted because of collinearity.{p_end}
{p 0 6 2}note: {bf:mv_asian} omitted because of collinearity.{p_end}
{p 0 6 2}note: {bf:mv_bluecollar} omitted because of collinearity.{p_end}
{p 0 6 2}note: {bf:mv_serviceworker} omitted because of collinearity.{p_end}
{p 0 6 2}note: {bf:4.country} omitted because of collinearity.{p_end}
{p 0 6 2}note: {bf:7.country} omitted because of collinearity.{p_end}
{p 0 6 2}note: {bf:9.country} omitted because of collinearity.{p_end}
{p 0 6 2}note: {bf:11.country} omitted because of collinearity.{p_end}
{p 0 6 2}note: {bf:12.country} omitted because of collinearity.{p_end}

Linear regression                               Number of obs     = {res}    22,541
                                                {txt}F(43, 22497)      =  {res}   384.50
                                                {txt}Prob > F          = {res}    0.0000
                                                {txt}R-squared         = {res}    0.4332
                                                {txt}Root MSE          =    {res} .20301

{txt}{hline 24}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 25}{c |}{col 37}    Robust
{col 1}              satis_dem{col 25}{c |} Coefficient{col 37}  std. err.{col 49}      t{col 57}   P>|t|{col 65}     [95% con{col 78}f. interval]
{hline 24}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 13}satis_head {c |}{col 25}{res}{space 2} .5200576{col 37}{space 2} .0052277{col 48}{space 1}   99.48{col 57}{space 3}0.000{col 65}{space 4}  .509811{col 78}{space 3} .5303042
{txt}{space 15}thirties {c |}{col 25}{res}{space 2}-.0155213{col 37}{space 2} .0046575{col 48}{space 1}   -3.33{col 57}{space 3}0.001{col 65}{space 4}-.0246504{col 78}{space 3}-.0063923
{txt}{space 15}fourties {c |}{col 25}{res}{space 2}-.0119015{col 37}{space 2} .0047149{col 48}{space 1}   -2.52{col 57}{space 3}0.012{col 65}{space 4}-.0211429{col 78}{space 3}  -.00266
{txt}{space 16}fifties {c |}{col 25}{res}{space 2}-.0110973{col 37}{space 2} .0049928{col 48}{space 1}   -2.22{col 57}{space 3}0.026{col 65}{space 4}-.0208835{col 78}{space 3}-.0013111
{txt}{space 16}sixties {c |}{col 25}{res}{space 2} .0030756{col 37}{space 2} .0049658{col 48}{space 1}    0.62{col 57}{space 3}0.536{col 65}{space 4}-.0066578{col 78}{space 3} .0128089
{txt}{space 14}seventies {c |}{col 25}{res}{space 2} .0200877{col 37}{space 2}  .005983{col 48}{space 1}    3.36{col 57}{space 3}0.001{col 65}{space 4} .0083606{col 78}{space 3} .0318147
{txt}{space 8}income2quartile {c |}{col 25}{res}{space 2} .0193694{col 37}{space 2} .0040108{col 48}{space 1}    4.83{col 57}{space 3}0.000{col 65}{space 4}  .011508{col 78}{space 3} .0272308
{txt}{space 8}income3quartile {c |}{col 25}{res}{space 2} .0259325{col 37}{space 2} .0041293{col 48}{space 1}    6.28{col 57}{space 3}0.000{col 65}{space 4} .0178387{col 78}{space 3} .0340262
{txt}{space 8}income4quartile {c |}{col 25}{res}{space 2} .0418597{col 37}{space 2} .0042184{col 48}{space 1}    9.92{col 57}{space 3}0.000{col 65}{space 4} .0335914{col 78}{space 3}  .050128
{txt}{space 9}incomenoanswer {c |}{col 25}{res}{space 2} .0079058{col 37}{space 2} .0065571{col 48}{space 1}    1.21{col 57}{space 3}0.228{col 65}{space 4}-.0049465{col 78}{space 3} .0207582
{txt}{space 17}female {c |}{col 25}{res}{space 2}-.0090972{col 37}{space 2} .0027628{col 48}{space 1}   -3.29{col 57}{space 3}0.001{col 65}{space 4}-.0145124{col 78}{space 3}-.0036819
{txt}{space 13}highschool {c |}{col 25}{res}{space 2} .0055054{col 37}{space 2} .0045648{col 48}{space 1}    1.21{col 57}{space 3}0.228{col 65}{space 4} -.003442{col 78}{space 3} .0144529
{txt}{space 16}college {c |}{col 25}{res}{space 2} .0270908{col 37}{space 2} .0042569{col 48}{space 1}    6.36{col 57}{space 3}0.000{col 65}{space 4}  .018747{col 78}{space 3} .0354347
{txt}{space 13}noreligion {c |}{col 25}{res}{space 2}-.0120853{col 37}{space 2} .0061742{col 48}{space 1}   -1.96{col 57}{space 3}0.050{col 65}{space 4}-.0241872{col 78}{space 3} .0000166
{txt}{space 3}christiannotcatholic {c |}{col 25}{res}{space 2} .0129874{col 37}{space 2} .0071514{col 48}{space 1}    1.82{col 57}{space 3}0.069{col 65}{space 4}-.0010299{col 78}{space 3} .0270047
{txt}{space 15}catholic {c |}{col 25}{res}{space 2} .0166084{col 37}{space 2} .0063256{col 48}{space 1}    2.63{col 57}{space 3}0.009{col 65}{space 4} .0042099{col 78}{space 3}  .029007
{txt}{space 9}fulltimeworker {c |}{col 25}{res}{space 2}-.0357101{col 37}{space 2} .0070242{col 48}{space 1}   -5.08{col 57}{space 3}0.000{col 65}{space 4} -.049478{col 78}{space 3}-.0219421
{txt}{space 9}parttimeworker {c |}{col 25}{res}{space 2}-.0094773{col 37}{space 2} .0182334{col 48}{space 1}   -0.52{col 57}{space 3}0.603{col 65}{space 4} -.045216{col 78}{space 3} .0262613
{txt}{space 13}unemployed {c |}{col 25}{res}{space 2}-.0467058{col 37}{space 2} .0109024{col 48}{space 1}   -4.28{col 57}{space 3}0.000{col 65}{space 4}-.0680753{col 78}{space 3}-.0253364
{txt}{space 11}selfemployed {c |}{col 25}{res}{space 2}-.0242525{col 37}{space 2} .0311783{col 48}{space 1}   -0.78{col 57}{space 3}0.437{col 65}{space 4}-.0853641{col 78}{space 3} .0368591
{txt}{space 16}outofLF {c |}{col 25}{res}{space 2}-.0353914{col 37}{space 2} .0075864{col 48}{space 1}   -4.67{col 57}{space 3}0.000{col 65}{space 4}-.0502611{col 78}{space 3}-.0205216
{txt}{space 13}goodhealth {c |}{col 25}{res}{space 2} .0255537{col 37}{space 2} .0029176{col 48}{space 1}    8.76{col 57}{space 3}0.000{col 65}{space 4} .0198351{col 78}{space 3} .0312724
{txt}{space 18}white {c |}{col 25}{res}{space 2}-.0096864{col 37}{space 2} .0353893{col 48}{space 1}   -0.27{col 57}{space 3}0.784{col 65}{space 4} -.079052{col 78}{space 3} .0596792
{txt}{space 18}black {c |}{col 25}{res}{space 2}  .067059{col 37}{space 2} .0385798{col 48}{space 1}    1.74{col 57}{space 3}0.082{col 65}{space 4}  -.00856{col 78}{space 3}  .142678
{txt}{space 17}latino {c |}{col 25}{res}{space 2} .0550436{col 37}{space 2} .0403937{col 48}{space 1}    1.36{col 57}{space 3}0.173{col 65}{space 4}-.0241308{col 78}{space 3}  .134218
{txt}{space 18}asian {c |}{col 25}{res}{space 2} .0350506{col 37}{space 2} .0399622{col 48}{space 1}    0.88{col 57}{space 3}0.380{col 65}{space 4} -.043278{col 78}{space 3} .1133792
{txt}{space 12}whitecollar {c |}{col 25}{res}{space 2} .0014873{col 37}{space 2} .0072333{col 48}{space 1}    0.21{col 57}{space 3}0.837{col 65}{space 4}-.0126904{col 78}{space 3}  .015665
{txt}{space 13}bluecollar {c |}{col 25}{res}{space 2}-.0103078{col 37}{space 2} .0073787{col 48}{space 1}   -1.40{col 57}{space 3}0.162{col 65}{space 4}-.0247705{col 78}{space 3}  .004155
{txt}{space 10}serviceworker {c |}{col 25}{res}{space 2}-.0105196{col 37}{space 2} .0058062{col 48}{space 1}   -1.81{col 57}{space 3}0.070{col 65}{space 4}-.0219002{col 78}{space 3} .0008609
{txt}{space 12}mv_thirties {c |}{col 25}{res}{space 2}        0{col 37}{txt}  (omitted)
{space 12}mv_fourties {c |}{col 25}{res}{space 2}        0{col 37}{txt}  (omitted)
{space 13}mv_fifties {c |}{col 25}{res}{space 2}        0{col 37}{txt}  (omitted)
{space 13}mv_sixties {c |}{col 25}{res}{space 2}        0{col 37}{txt}  (omitted)
{space 11}mv_seventies {c |}{col 25}{res}{space 2}        0{col 37}{txt}  (omitted)
{space 5}mv_income2quartile {c |}{col 25}{res}{space 2}        0{col 37}{txt}  (omitted)
{space 5}mv_income3quartile {c |}{col 25}{res}{space 2}        0{col 37}{txt}  (omitted)
{space 5}mv_income4quartile {c |}{col 25}{res}{space 2}        0{col 37}{txt}  (omitted)
{space 6}mv_incomenoanswer {c |}{col 25}{res}{space 2} .0285057{col 37}{space 2} .0083943{col 48}{space 1}    3.40{col 57}{space 3}0.001{col 65}{space 4} .0120522{col 78}{space 3} .0449591
{txt}{space 14}mv_female {c |}{col 25}{res}{space 2}        0{col 37}{txt}  (omitted)
{space 10}mv_highschool {c |}{col 25}{res}{space 2} .0271321{col 37}{space 2} .0105675{col 48}{space 1}    2.57{col 57}{space 3}0.010{col 65}{space 4} .0064191{col 78}{space 3}  .047845
{txt}{space 13}mv_college {c |}{col 25}{res}{space 2}        0{col 37}{txt}  (omitted)
{space 10}mv_noreligion {c |}{col 25}{res}{space 2}  .000578{col 37}{space 2} .0232722{col 48}{space 1}    0.02{col 57}{space 3}0.980{col 65}{space 4}-.0450372{col 78}{space 3} .0461932
{txt}mv_christiannotcatholic {c |}{col 25}{res}{space 2}        0{col 37}{txt}  (omitted)
{space 12}mv_catholic {c |}{col 25}{res}{space 2}        0{col 37}{txt}  (omitted)
{space 6}mv_fulltimeworker {c |}{col 25}{res}{space 2}        0{col 37}{txt}  (omitted)
{space 6}mv_parttimeworker {c |}{col 25}{res}{space 2}-.0182405{col 37}{space 2} .0143302{col 48}{space 1}   -1.27{col 57}{space 3}0.203{col 65}{space 4}-.0463287{col 78}{space 3} .0098478
{txt}{space 10}mv_unemployed {c |}{col 25}{res}{space 2}        0{col 37}{txt}  (omitted)
{space 8}mv_selfemployed {c |}{col 25}{res}{space 2} .0332709{col 37}{space 2} .0155712{col 48}{space 1}    2.14{col 57}{space 3}0.033{col 65}{space 4} .0027503{col 78}{space 3} .0637915
{txt}{space 13}mv_outofLF {c |}{col 25}{res}{space 2}        0{col 37}{txt}  (omitted)
{space 10}mv_goodhealth {c |}{col 25}{res}{space 2} .1199165{col 37}{space 2} .0721463{col 48}{space 1}    1.66{col 57}{space 3}0.097{col 65}{space 4}-.0214953{col 78}{space 3} .2613284
{txt}{space 15}mv_white {c |}{col 25}{res}{space 2} .0336298{col 37}{space 2} .0352031{col 48}{space 1}    0.96{col 57}{space 3}0.339{col 65}{space 4}-.0353707{col 78}{space 3} .1026304
{txt}{space 15}mv_black {c |}{col 25}{res}{space 2}        0{col 37}{txt}  (omitted)
{space 14}mv_latino {c |}{col 25}{res}{space 2}        0{col 37}{txt}  (omitted)
{space 15}mv_asian {c |}{col 25}{res}{space 2}        0{col 37}{txt}  (omitted)
{space 9}mv_whitecollar {c |}{col 25}{res}{space 2}-.0212791{col 37}{space 2} .0138928{col 48}{space 1}   -1.53{col 57}{space 3}0.126{col 65}{space 4}  -.04851{col 78}{space 3} .0059518
{txt}{space 10}mv_bluecollar {c |}{col 25}{res}{space 2}        0{col 37}{txt}  (omitted)
{space 7}mv_serviceworker {c |}{col 25}{res}{space 2}        0{col 37}{txt}  (omitted)
{space 23} {c |}
{space 16}country {c |}
{space 15}Austria  {c |}{col 25}{res}{space 2} .0456118{col 37}{space 2} .0378696{col 48}{space 1}    1.20{col 57}{space 3}0.228{col 65}{space 4}-.0286153{col 78}{space 3} .1198389
{txt}{space 16}Brazil  {c |}{col 25}{res}{space 2}-.0733477{col 37}{space 2} .0147285{col 48}{space 1}   -4.98{col 57}{space 3}0.000{col 65}{space 4}-.1022166{col 78}{space 3}-.0444787
{txt}{space 16}France  {c |}{col 25}{res}{space 2}        0{col 37}{txt}  (omitted)
{space 15}Germany  {c |}{col 25}{res}{space 2} .0247377{col 37}{space 2} .0375266{col 48}{space 1}    0.66{col 57}{space 3}0.510{col 65}{space 4}-.0488171{col 78}{space 3} .0982924
{txt}{space 17}Italy  {c |}{col 25}{res}{space 2}-.0670186{col 37}{space 2} .0439194{col 48}{space 1}   -1.53{col 57}{space 3}0.127{col 65}{space 4}-.1531036{col 78}{space 3} .0190664
{txt}{space 11}New_Zealand  {c |}{col 25}{res}{space 2}        0{col 37}{txt}  (omitted)
{space 16}Poland  {c |}{col 25}{res}{space 2}-.0951351{col 37}{space 2} .0137868{col 48}{space 1}   -6.90{col 57}{space 3}0.000{col 65}{space 4}-.1221582{col 78}{space 3} -.068112
{txt}{space 17}Spain  {c |}{col 25}{res}{space 2}        0{col 37}{txt}  (omitted)
{space 16}Sweden  {c |}{col 25}{res}{space 2} .0630135{col 37}{space 2} .0138525{col 48}{space 1}    4.55{col 57}{space 3}0.000{col 65}{space 4} .0358617{col 78}{space 3} .0901654
{txt}{space 20}UK  {c |}{col 25}{res}{space 2}        0{col 37}{txt}  (omitted)
{space 20}US  {c |}{col 25}{res}{space 2}        0{col 37}{txt}  (omitted)
{space 23} {c |}
{space 18}_cons {c |}{col 25}{res}{space 2} .2057128{col 37}{space 2} .0386899{col 48}{space 1}    5.32{col 57}{space 3}0.000{col 65}{space 4} .1298778{col 78}{space 3} .2815477
{txt}{hline 24}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{res}{txt}({res}est2{txt} stored)

{com}. get_mean

{txt}added scalar:
                 e(av) =  {res}.5
{txt}
{com}. estadd local Controls "X", replace

{txt}added macro:
           e(Controls) : "{res:X}"

{com}. estadd local CFE "X", replace

{txt}added macro:
                e(CFE) : "{res:X}"

{com}. 
. eststo: reg democ satis_head,  robust 

{txt}Linear regression                               Number of obs     = {res}    22,537
                                                {txt}F(1, 22535)       =  {res}    57.72
                                                {txt}Prob > F          = {res}    0.0000
                                                {txt}R-squared         = {res}    0.0024
                                                {txt}Root MSE          =    {res} .29663

{txt}{hline 13}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 14}{c |}{col 26}    Robust
{col 1}       democ{col 14}{c |} Coefficient{col 26}  std. err.{col 38}      t{col 46}   P>|t|{col 54}     [95% con{col 67}f. interval]
{hline 13}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 2}satis_head {c |}{col 14}{res}{space 2} .0463228{col 26}{space 2} .0060972{col 37}{space 1}    7.60{col 46}{space 3}0.000{col 54}{space 4} .0343718{col 67}{space 3} .0582737
{txt}{space 7}_cons {c |}{col 14}{res}{space 2} .8810234{col 26}{space 2} .0036402{col 37}{space 1}  242.02{col 46}{space 3}0.000{col 54}{space 4} .8738883{col 67}{space 3} .8881585
{txt}{hline 13}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{res}{txt}({res}est3{txt} stored)

{com}. get_mean

{txt}added scalar:
                 e(av) =  {res}.902
{txt}
{com}. 
. eststo: reg democ satis_head  $controls $mv_controls i.country,  robust 
{txt}{p 0 6 2}note: {bf:mv_thirties} omitted because of collinearity.{p_end}
{p 0 6 2}note: {bf:mv_fourties} omitted because of collinearity.{p_end}
{p 0 6 2}note: {bf:mv_fifties} omitted because of collinearity.{p_end}
{p 0 6 2}note: {bf:mv_sixties} omitted because of collinearity.{p_end}
{p 0 6 2}note: {bf:mv_seventies} omitted because of collinearity.{p_end}
{p 0 6 2}note: {bf:mv_income2quartile} omitted because of collinearity.{p_end}
{p 0 6 2}note: {bf:mv_income3quartile} omitted because of collinearity.{p_end}
{p 0 6 2}note: {bf:mv_income4quartile} omitted because of collinearity.{p_end}
{p 0 6 2}note: {bf:mv_female} omitted because of collinearity.{p_end}
{p 0 6 2}note: {bf:mv_college} omitted because of collinearity.{p_end}
{p 0 6 2}note: {bf:mv_christiannotcatholic} omitted because of collinearity.{p_end}
{p 0 6 2}note: {bf:mv_catholic} omitted because of collinearity.{p_end}
{p 0 6 2}note: {bf:mv_fulltimeworker} omitted because of collinearity.{p_end}
{p 0 6 2}note: {bf:mv_unemployed} omitted because of collinearity.{p_end}
{p 0 6 2}note: {bf:mv_outofLF} omitted because of collinearity.{p_end}
{p 0 6 2}note: {bf:mv_black} omitted because of collinearity.{p_end}
{p 0 6 2}note: {bf:mv_latino} omitted because of collinearity.{p_end}
{p 0 6 2}note: {bf:mv_asian} omitted because of collinearity.{p_end}
{p 0 6 2}note: {bf:mv_bluecollar} omitted because of collinearity.{p_end}
{p 0 6 2}note: {bf:mv_serviceworker} omitted because of collinearity.{p_end}
{p 0 6 2}note: {bf:4.country} omitted because of collinearity.{p_end}
{p 0 6 2}note: {bf:7.country} omitted because of collinearity.{p_end}
{p 0 6 2}note: {bf:9.country} omitted because of collinearity.{p_end}
{p 0 6 2}note: {bf:11.country} omitted because of collinearity.{p_end}
{p 0 6 2}note: {bf:12.country} omitted because of collinearity.{p_end}

Linear regression                               Number of obs     = {res}    22,537
                                                {txt}F(43, 22493)      =  {res}    19.99
                                                {txt}Prob > F          = {res}    0.0000
                                                {txt}R-squared         = {res}    0.0438
                                                {txt}Root MSE          =    {res} .29068

{txt}{hline 24}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 25}{c |}{col 37}    Robust
{col 1}                  democ{col 25}{c |} Coefficient{col 37}  std. err.{col 49}      t{col 57}   P>|t|{col 65}     [95% con{col 78}f. interval]
{hline 24}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 13}satis_head {c |}{col 25}{res}{space 2} .0335735{col 37}{space 2} .0067258{col 48}{space 1}    4.99{col 57}{space 3}0.000{col 65}{space 4} .0203905{col 78}{space 3} .0467566
{txt}{space 15}thirties {c |}{col 25}{res}{space 2} .0051691{col 37}{space 2}  .007894{col 48}{space 1}    0.65{col 57}{space 3}0.513{col 65}{space 4}-.0103037{col 78}{space 3} .0206419
{txt}{space 15}fourties {c |}{col 25}{res}{space 2} .0141252{col 37}{space 2} .0076966{col 48}{space 1}    1.84{col 57}{space 3}0.066{col 65}{space 4}-.0009606{col 78}{space 3}  .029211
{txt}{space 16}fifties {c |}{col 25}{res}{space 2} .0439379{col 37}{space 2} .0076948{col 48}{space 1}    5.71{col 57}{space 3}0.000{col 65}{space 4} .0288555{col 78}{space 3} .0590203
{txt}{space 16}sixties {c |}{col 25}{res}{space 2} .0664942{col 37}{space 2} .0074543{col 48}{space 1}    8.92{col 57}{space 3}0.000{col 65}{space 4} .0518832{col 78}{space 3} .0811052
{txt}{space 14}seventies {c |}{col 25}{res}{space 2} .0831303{col 37}{space 2} .0080178{col 48}{space 1}   10.37{col 57}{space 3}0.000{col 65}{space 4} .0674148{col 78}{space 3} .0988458
{txt}{space 8}income2quartile {c |}{col 25}{res}{space 2} .0293835{col 37}{space 2} .0062735{col 48}{space 1}    4.68{col 57}{space 3}0.000{col 65}{space 4} .0170869{col 78}{space 3}   .04168
{txt}{space 8}income3quartile {c |}{col 25}{res}{space 2} .0485098{col 37}{space 2}  .006272{col 48}{space 1}    7.73{col 57}{space 3}0.000{col 65}{space 4} .0362163{col 78}{space 3} .0608033
{txt}{space 8}income4quartile {c |}{col 25}{res}{space 2} .0538406{col 37}{space 2} .0061482{col 48}{space 1}    8.76{col 57}{space 3}0.000{col 65}{space 4} .0417897{col 78}{space 3} .0658915
{txt}{space 9}incomenoanswer {c |}{col 25}{res}{space 2} .0078133{col 37}{space 2} .0096051{col 48}{space 1}    0.81{col 57}{space 3}0.416{col 65}{space 4}-.0110134{col 78}{space 3} .0266399
{txt}{space 17}female {c |}{col 25}{res}{space 2}-.0094598{col 37}{space 2} .0039821{col 48}{space 1}   -2.38{col 57}{space 3}0.018{col 65}{space 4}-.0172649{col 78}{space 3}-.0016546
{txt}{space 13}highschool {c |}{col 25}{res}{space 2} .0245816{col 37}{space 2}   .00727{col 48}{space 1}    3.38{col 57}{space 3}0.001{col 65}{space 4} .0103319{col 78}{space 3} .0388313
{txt}{space 16}college {c |}{col 25}{res}{space 2} .0671883{col 37}{space 2} .0066287{col 48}{space 1}   10.14{col 57}{space 3}0.000{col 65}{space 4} .0541956{col 78}{space 3} .0801809
{txt}{space 13}noreligion {c |}{col 25}{res}{space 2} .0373005{col 37}{space 2} .0097627{col 48}{space 1}    3.82{col 57}{space 3}0.000{col 65}{space 4} .0181649{col 78}{space 3} .0564361
{txt}{space 3}christiannotcatholic {c |}{col 25}{res}{space 2} .0337594{col 37}{space 2} .0107803{col 48}{space 1}    3.13{col 57}{space 3}0.002{col 65}{space 4} .0126292{col 78}{space 3} .0548896
{txt}{space 15}catholic {c |}{col 25}{res}{space 2} .0369617{col 37}{space 2} .0098482{col 48}{space 1}    3.75{col 57}{space 3}0.000{col 65}{space 4} .0176587{col 78}{space 3} .0562648
{txt}{space 9}fulltimeworker {c |}{col 25}{res}{space 2}-.0048964{col 37}{space 2} .0097942{col 48}{space 1}   -0.50{col 57}{space 3}0.617{col 65}{space 4}-.0240937{col 78}{space 3} .0143009
{txt}{space 9}parttimeworker {c |}{col 25}{res}{space 2} .0213818{col 37}{space 2}  .022385{col 48}{space 1}    0.96{col 57}{space 3}0.339{col 65}{space 4}-.0224945{col 78}{space 3}  .065258
{txt}{space 13}unemployed {c |}{col 25}{res}{space 2}-.0109051{col 37}{space 2} .0169758{col 48}{space 1}   -0.64{col 57}{space 3}0.521{col 65}{space 4}-.0441788{col 78}{space 3} .0223687
{txt}{space 11}selfemployed {c |}{col 25}{res}{space 2}-.0116388{col 37}{space 2} .0316541{col 48}{space 1}   -0.37{col 57}{space 3}0.713{col 65}{space 4} -.073683{col 78}{space 3} .0504054
{txt}{space 16}outofLF {c |}{col 25}{res}{space 2} .0068904{col 37}{space 2} .0102245{col 48}{space 1}    0.67{col 57}{space 3}0.500{col 65}{space 4}-.0131503{col 78}{space 3} .0269311
{txt}{space 13}goodhealth {c |}{col 25}{res}{space 2}  .030923{col 37}{space 2} .0044647{col 48}{space 1}    6.93{col 57}{space 3}0.000{col 65}{space 4} .0221719{col 78}{space 3} .0396741
{txt}{space 18}white {c |}{col 25}{res}{space 2} .0596972{col 37}{space 2} .0578938{col 48}{space 1}    1.03{col 57}{space 3}0.302{col 65}{space 4}-.0537787{col 78}{space 3}  .173173
{txt}{space 18}black {c |}{col 25}{res}{space 2}  .070261{col 37}{space 2} .0633709{col 48}{space 1}    1.11{col 57}{space 3}0.268{col 65}{space 4}-.0539504{col 78}{space 3} .1944724
{txt}{space 17}latino {c |}{col 25}{res}{space 2} .0989731{col 37}{space 2} .0674747{col 48}{space 1}    1.47{col 57}{space 3}0.142{col 65}{space 4}-.0332821{col 78}{space 3} .2312282
{txt}{space 18}asian {c |}{col 25}{res}{space 2} .0866868{col 37}{space 2} .0664166{col 48}{space 1}    1.31{col 57}{space 3}0.192{col 65}{space 4}-.0434943{col 78}{space 3}  .216868
{txt}{space 12}whitecollar {c |}{col 25}{res}{space 2}-.0131975{col 37}{space 2} .0096212{col 48}{space 1}   -1.37{col 57}{space 3}0.170{col 65}{space 4}-.0320557{col 78}{space 3} .0056606
{txt}{space 13}bluecollar {c |}{col 25}{res}{space 2}-.0223337{col 37}{space 2} .0113621{col 48}{space 1}   -1.97{col 57}{space 3}0.049{col 65}{space 4}-.0446041{col 78}{space 3}-.0000632
{txt}{space 10}serviceworker {c |}{col 25}{res}{space 2} -.011966{col 37}{space 2}  .008397{col 48}{space 1}   -1.43{col 57}{space 3}0.154{col 65}{space 4}-.0284247{col 78}{space 3} .0044927
{txt}{space 12}mv_thirties {c |}{col 25}{res}{space 2}        0{col 37}{txt}  (omitted)
{space 12}mv_fourties {c |}{col 25}{res}{space 2}        0{col 37}{txt}  (omitted)
{space 13}mv_fifties {c |}{col 25}{res}{space 2}        0{col 37}{txt}  (omitted)
{space 13}mv_sixties {c |}{col 25}{res}{space 2}        0{col 37}{txt}  (omitted)
{space 11}mv_seventies {c |}{col 25}{res}{space 2}        0{col 37}{txt}  (omitted)
{space 5}mv_income2quartile {c |}{col 25}{res}{space 2}        0{col 37}{txt}  (omitted)
{space 5}mv_income3quartile {c |}{col 25}{res}{space 2}        0{col 37}{txt}  (omitted)
{space 5}mv_income4quartile {c |}{col 25}{res}{space 2}        0{col 37}{txt}  (omitted)
{space 6}mv_incomenoanswer {c |}{col 25}{res}{space 2}-.0045547{col 37}{space 2}  .012626{col 48}{space 1}   -0.36{col 57}{space 3}0.718{col 65}{space 4}-.0293025{col 78}{space 3} .0201931
{txt}{space 14}mv_female {c |}{col 25}{res}{space 2}        0{col 37}{txt}  (omitted)
{space 10}mv_highschool {c |}{col 25}{res}{space 2} .0243579{col 37}{space 2} .0175037{col 48}{space 1}    1.39{col 57}{space 3}0.164{col 65}{space 4}-.0099505{col 78}{space 3} .0586664
{txt}{space 13}mv_college {c |}{col 25}{res}{space 2}        0{col 37}{txt}  (omitted)
{space 10}mv_noreligion {c |}{col 25}{res}{space 2} .0320999{col 37}{space 2} .0266307{col 48}{space 1}    1.21{col 57}{space 3}0.228{col 65}{space 4}-.0200982{col 78}{space 3}  .084298
{txt}mv_christiannotcatholic {c |}{col 25}{res}{space 2}        0{col 37}{txt}  (omitted)
{space 12}mv_catholic {c |}{col 25}{res}{space 2}        0{col 37}{txt}  (omitted)
{space 6}mv_fulltimeworker {c |}{col 25}{res}{space 2}        0{col 37}{txt}  (omitted)
{space 6}mv_parttimeworker {c |}{col 25}{res}{space 2} .0191614{col 37}{space 2}  .020904{col 48}{space 1}    0.92{col 57}{space 3}0.359{col 65}{space 4}-.0218118{col 78}{space 3} .0601347
{txt}{space 10}mv_unemployed {c |}{col 25}{res}{space 2}        0{col 37}{txt}  (omitted)
{space 8}mv_selfemployed {c |}{col 25}{res}{space 2}  -.04618{col 37}{space 2} .0214976{col 48}{space 1}   -2.15{col 57}{space 3}0.032{col 65}{space 4}-.0883167{col 78}{space 3}-.0040432
{txt}{space 13}mv_outofLF {c |}{col 25}{res}{space 2}        0{col 37}{txt}  (omitted)
{space 10}mv_goodhealth {c |}{col 25}{res}{space 2}-.1717267{col 37}{space 2} .1333277{col 48}{space 1}   -1.29{col 57}{space 3}0.198{col 65}{space 4}-.4330583{col 78}{space 3}  .089605
{txt}{space 15}mv_white {c |}{col 25}{res}{space 2} .1547539{col 37}{space 2} .0578839{col 48}{space 1}    2.67{col 57}{space 3}0.008{col 65}{space 4} .0412975{col 78}{space 3} .2682104
{txt}{space 15}mv_black {c |}{col 25}{res}{space 2}        0{col 37}{txt}  (omitted)
{space 14}mv_latino {c |}{col 25}{res}{space 2}        0{col 37}{txt}  (omitted)
{space 15}mv_asian {c |}{col 25}{res}{space 2}        0{col 37}{txt}  (omitted)
{space 9}mv_whitecollar {c |}{col 25}{res}{space 2}-.0496804{col 37}{space 2} .0210297{col 48}{space 1}   -2.36{col 57}{space 3}0.018{col 65}{space 4}   -.0909{col 78}{space 3}-.0084608
{txt}{space 10}mv_bluecollar {c |}{col 25}{res}{space 2}        0{col 37}{txt}  (omitted)
{space 7}mv_serviceworker {c |}{col 25}{res}{space 2}        0{col 37}{txt}  (omitted)
{space 23} {c |}
{space 16}country {c |}
{space 15}Austria  {c |}{col 25}{res}{space 2} .1558568{col 37}{space 2} .0612953{col 48}{space 1}    2.54{col 57}{space 3}0.011{col 65}{space 4} .0357136{col 78}{space 3} .2759999
{txt}{space 16}Brazil  {c |}{col 25}{res}{space 2}-.0710323{col 37}{space 2} .0214729{col 48}{space 1}   -3.31{col 57}{space 3}0.001{col 65}{space 4}-.1131206{col 78}{space 3} -.028944
{txt}{space 16}France  {c |}{col 25}{res}{space 2}        0{col 37}{txt}  (omitted)
{space 15}Germany  {c |}{col 25}{res}{space 2} .1002479{col 37}{space 2} .0613857{col 48}{space 1}    1.63{col 57}{space 3}0.102{col 65}{space 4}-.0200722{col 78}{space 3} .2205681
{txt}{space 17}Italy  {c |}{col 25}{res}{space 2} .1381405{col 37}{space 2} .0664645{col 48}{space 1}    2.08{col 57}{space 3}0.038{col 65}{space 4} .0078654{col 78}{space 3} .2684155
{txt}{space 11}New_Zealand  {c |}{col 25}{res}{space 2}        0{col 37}{txt}  (omitted)
{space 16}Poland  {c |}{col 25}{res}{space 2}-.1079314{col 37}{space 2}    .0223{col 48}{space 1}   -4.84{col 57}{space 3}0.000{col 65}{space 4}-.1516409{col 78}{space 3}-.0642219
{txt}{space 17}Spain  {c |}{col 25}{res}{space 2}        0{col 37}{txt}  (omitted)
{space 16}Sweden  {c |}{col 25}{res}{space 2}-.0626136{col 37}{space 2} .0206412{col 48}{space 1}   -3.03{col 57}{space 3}0.002{col 65}{space 4}-.1030718{col 78}{space 3}-.0221555
{txt}{space 20}UK  {c |}{col 25}{res}{space 2}        0{col 37}{txt}  (omitted)
{space 20}US  {c |}{col 25}{res}{space 2}        0{col 37}{txt}  (omitted)
{space 23} {c |}
{space 18}_cons {c |}{col 25}{res}{space 2} .6630718{col 37}{space 2} .0625691{col 48}{space 1}   10.60{col 57}{space 3}0.000{col 65}{space 4}  .540432{col 78}{space 3} .7857115
{txt}{hline 24}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{res}{txt}({res}est4{txt} stored)

{com}. get_mean

{txt}added scalar:
                 e(av) =  {res}.902
{txt}
{com}. estadd local Controls "X", replace

{txt}added macro:
           e(Controls) : "{res:X}"

{com}. estadd local CFE "X", replace

{txt}added macro:
                e(CFE) : "{res:X}"

{com}. 
. esttab using "3_output/2_OA/Tables/TableE7.tex", depvar nocons keep(satis_head) ///
>          label replace wrap ///
>         cells(b(star fmt(%15.3fc)) se(par fmt(%15.3fc))) interaction(" $\times$ ") ///
>         star(* 0.10 ** 0.05 *** 0.01) ///
>         stats(Controls CFE N av   , layout(@ @ @ @ ) fmt(%15s %15s %15.0fc %9.3f  ) ///
>         labels("Individual controls" "Country FE" Observations "Outcome mean" )) ///
>         collabels(none) mlabels(none) ///
>         mgroups("Satisfaction with democracy" "Democracy (support)"  , pattern(1 0 1 0 ) ///
>         prefix(\multicolumn{c -(}@span{c )-}{c -(}c{c )-}{c -(}) suffix({c )-}) span )
{res}{txt}(output written to {browse  `"3_output/2_OA/Tables/TableE7.tex"'})

{com}. 
. 
.                         
. **# Table E.8. Impact on political efficacy - 2SLS.
. 
. eststo clear
{txt}
{com}. /* E.8. Col 1: External Efficacy with 16 instruments */
. eststo: ivreg2 gov_care_inv ///
>         (satis_head = TH1_TE1 TH1_TE2 TH1_TE3 TH1_TE4 TH2_TE1 TH2_TE2 TH2_TE3 TH2_TE4 TH3_TE1 TH3_TE2 TH3_TE3 TH3_TE4 TH4_TE1 TH4_TE2 TH4_TE3) $controls $mv_controls  i.country, small robust 
{txt}Warning - collinearities detected
Vars dropped:{col 21}mv_thirties mv_fourties mv_fifties mv_sixties mv_seventies
{col 21}mv_income2quartile mv_income3quartile mv_income4quartile
{col 21}mv_female mv_college mv_christiannotcatholic mv_catholic
{col 21}mv_fulltimeworker mv_unemployed mv_outofLF mv_black
{col 21}mv_latino mv_asian mv_bluecollar mv_serviceworker 4.country
{col 21}7.country 9.country 11.country 12.country
{res}
{txt}IV (2SLS) estimation
{hline 20}

Estimates efficient for homoskedasticity only
Statistics robust to heteroskedasticity

{col 55}Number of obs = {res}   22538
{txt}{col 55}F( 43, 22494) = {res}   28.68
{txt}{col 55}Prob > F      = {res}  0.0000
{txt}Total (centered) SS     = {res} 4833.829843{txt}{col 55}Centered R2   = {res}  0.1498
{txt}Total (uncentered) SS   = {res}        7021{txt}{col 55}Uncentered R2 = {res}  0.4147
{txt}Residual SS             = {res} 4109.507341{txt}{col 55}Root MSE      = {res}   .4274

{txt}{hline 24}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 25}{c |}{col 37}    Robust
{col 1}           gov_care_inv{col 25}{c |} Coefficient{col 37}  std. err.{col 49}      t{col 57}   P>|t|{col 65}     [95% con{col 78}f. interval]
{hline 24}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 13}satis_head {c |}{col 25}{res}{space 2} .4585297{col 37}{space 2} .3045232{col 48}{space 1}    1.51{col 57}{space 3}0.132{col 65}{space 4}-.1383569{col 78}{space 3} 1.055416
{txt}{space 15}thirties {c |}{col 25}{res}{space 2}-.0540821{col 37}{space 2} .0113957{col 48}{space 1}   -4.75{col 57}{space 3}0.000{col 65}{space 4}-.0764184{col 78}{space 3}-.0317459
{txt}{space 15}fourties {c |}{col 25}{res}{space 2}-.0425266{col 37}{space 2} .0141939{col 48}{space 1}   -3.00{col 57}{space 3}0.003{col 65}{space 4}-.0703476{col 78}{space 3}-.0147056
{txt}{space 16}fifties {c |}{col 25}{res}{space 2}-.0491618{col 37}{space 2} .0148333{col 48}{space 1}   -3.31{col 57}{space 3}0.001{col 65}{space 4}-.0782361{col 78}{space 3}-.0200875
{txt}{space 16}sixties {c |}{col 25}{res}{space 2}-.0755843{col 37}{space 2} .0152009{col 48}{space 1}   -4.97{col 57}{space 3}0.000{col 65}{space 4}-.1053792{col 78}{space 3}-.0457894
{txt}{space 14}seventies {c |}{col 25}{res}{space 2}-.0385182{col 37}{space 2} .0134843{col 48}{space 1}   -2.86{col 57}{space 3}0.004{col 65}{space 4}-.0649484{col 78}{space 3}-.0120881
{txt}{space 8}income2quartile {c |}{col 25}{res}{space 2} .0036753{col 37}{space 2} .0098301{col 48}{space 1}    0.37{col 57}{space 3}0.708{col 65}{space 4}-.0155925{col 78}{space 3}  .022943
{txt}{space 8}income3quartile {c |}{col 25}{res}{space 2}-.0037899{col 37}{space 2} .0109539{col 48}{space 1}   -0.35{col 57}{space 3}0.729{col 65}{space 4}-.0252602{col 78}{space 3} .0176805
{txt}{space 8}income4quartile {c |}{col 25}{res}{space 2} .0395693{col 37}{space 2} .0153844{col 48}{space 1}    2.57{col 57}{space 3}0.010{col 65}{space 4} .0094149{col 78}{space 3} .0697237
{txt}{space 9}incomenoanswer {c |}{col 25}{res}{space 2} .0196237{col 37}{space 2} .0140624{col 48}{space 1}    1.40{col 57}{space 3}0.163{col 65}{space 4}-.0079396{col 78}{space 3} .0471869
{txt}{space 17}female {c |}{col 25}{res}{space 2}-.0040851{col 37}{space 2} .0066728{col 48}{space 1}   -0.61{col 57}{space 3}0.540{col 65}{space 4}-.0171643{col 78}{space 3} .0089941
{txt}{space 13}highschool {c |}{col 25}{res}{space 2} .0005635{col 37}{space 2} .0098863{col 48}{space 1}    0.06{col 57}{space 3}0.955{col 65}{space 4}-.0188144{col 78}{space 3} .0199414
{txt}{space 16}college {c |}{col 25}{res}{space 2} .0320676{col 37}{space 2}  .008949{col 48}{space 1}    3.58{col 57}{space 3}0.000{col 65}{space 4}  .014527{col 78}{space 3} .0496082
{txt}{space 13}noreligion {c |}{col 25}{res}{space 2}-.0205953{col 37}{space 2} .0158086{col 48}{space 1}   -1.30{col 57}{space 3}0.193{col 65}{space 4}-.0515812{col 78}{space 3} .0103906
{txt}{space 3}christiannotcatholic {c |}{col 25}{res}{space 2}-.0071647{col 37}{space 2} .0241129{col 48}{space 1}   -0.30{col 57}{space 3}0.766{col 65}{space 4}-.0544277{col 78}{space 3} .0400983
{txt}{space 15}catholic {c |}{col 25}{res}{space 2}-.0208862{col 37}{space 2} .0142922{col 48}{space 1}   -1.46{col 57}{space 3}0.144{col 65}{space 4}-.0488999{col 78}{space 3} .0071276
{txt}{space 9}fulltimeworker {c |}{col 25}{res}{space 2}-.1095106{col 37}{space 2} .0966017{col 48}{space 1}   -1.13{col 57}{space 3}0.257{col 65}{space 4}-.2988567{col 78}{space 3} .0798355
{txt}{space 9}parttimeworker {c |}{col 25}{res}{space 2}-.0505507{col 37}{space 2} .0996528{col 48}{space 1}   -0.51{col 57}{space 3}0.612{col 65}{space 4}-.2458772{col 78}{space 3} .1447757
{txt}{space 13}unemployed {c |}{col 25}{res}{space 2}-.1172565{col 37}{space 2} .1077098{col 48}{space 1}   -1.09{col 57}{space 3}0.276{col 65}{space 4}-.3283752{col 78}{space 3} .0938623
{txt}{space 11}selfemployed {c |}{col 25}{res}{space 2}-.1012539{col 37}{space 2}     .121{col 48}{space 1}   -0.84{col 57}{space 3}0.403{col 65}{space 4}-.3384224{col 78}{space 3} .1359146
{txt}{space 16}outofLF {c |}{col 25}{res}{space 2}-.0868545{col 37}{space 2} .1002816{col 48}{space 1}   -0.87{col 57}{space 3}0.386{col 65}{space 4}-.2834134{col 78}{space 3} .1097045
{txt}{space 13}goodhealth {c |}{col 25}{res}{space 2} .0069956{col 37}{space 2} .0164453{col 48}{space 1}    0.43{col 57}{space 3}0.671{col 65}{space 4}-.0252384{col 78}{space 3} .0392296
{txt}{space 18}white {c |}{col 25}{res}{space 2}-.0695642{col 37}{space 2} .0690813{col 48}{space 1}   -1.01{col 57}{space 3}0.314{col 65}{space 4}-.2049684{col 78}{space 3} .0658401
{txt}{space 18}black {c |}{col 25}{res}{space 2} .0365024{col 37}{space 2}  .063809{col 48}{space 1}    0.57{col 57}{space 3}0.567{col 65}{space 4}-.0885676{col 78}{space 3} .1615724
{txt}{space 17}latino {c |}{col 25}{res}{space 2} .0674969{col 37}{space 2} .0741727{col 48}{space 1}    0.91{col 57}{space 3}0.363{col 65}{space 4}-.0778867{col 78}{space 3} .2128804
{txt}{space 18}asian {c |}{col 25}{res}{space 2} .0886117{col 37}{space 2} .0753289{col 48}{space 1}    1.18{col 57}{space 3}0.239{col 65}{space 4}-.0590382{col 78}{space 3} .2362617
{txt}{space 12}whitecollar {c |}{col 25}{res}{space 2}-.0010098{col 37}{space 2} .0160738{col 48}{space 1}   -0.06{col 57}{space 3}0.950{col 65}{space 4}-.0325156{col 78}{space 3} .0304961
{txt}{space 13}bluecollar {c |}{col 25}{res}{space 2}-.0773518{col 37}{space 2}   .01661{col 48}{space 1}   -4.66{col 57}{space 3}0.000{col 65}{space 4}-.1099086{col 78}{space 3} -.044795
{txt}{space 10}serviceworker {c |}{col 25}{res}{space 2} -.014108{col 37}{space 2} .0129113{col 48}{space 1}   -1.09{col 57}{space 3}0.275{col 65}{space 4}-.0394151{col 78}{space 3}  .011199
{txt}{space 12}mv_thirties {c |}{col 25}{res}{space 2}        0{col 37}{txt}  (omitted)
{space 12}mv_fourties {c |}{col 25}{res}{space 2}        0{col 37}{txt}  (omitted)
{space 13}mv_fifties {c |}{col 25}{res}{space 2}        0{col 37}{txt}  (omitted)
{space 13}mv_sixties {c |}{col 25}{res}{space 2}        0{col 37}{txt}  (omitted)
{space 11}mv_seventies {c |}{col 25}{res}{space 2}        0{col 37}{txt}  (omitted)
{space 5}mv_income2quartile {c |}{col 25}{res}{space 2}        0{col 37}{txt}  (omitted)
{space 5}mv_income3quartile {c |}{col 25}{res}{space 2}        0{col 37}{txt}  (omitted)
{space 5}mv_income4quartile {c |}{col 25}{res}{space 2}        0{col 37}{txt}  (omitted)
{space 6}mv_incomenoanswer {c |}{col 25}{res}{space 2} -.042238{col 37}{space 2} .0313235{col 48}{space 1}   -1.35{col 57}{space 3}0.178{col 65}{space 4}-.1036342{col 78}{space 3} .0191582
{txt}{space 14}mv_female {c |}{col 25}{res}{space 2}        0{col 37}{txt}  (omitted)
{space 10}mv_highschool {c |}{col 25}{res}{space 2} .0243726{col 37}{space 2} .0224643{col 48}{space 1}    1.08{col 57}{space 3}0.278{col 65}{space 4}-.0196591{col 78}{space 3} .0684042
{txt}{space 13}mv_college {c |}{col 25}{res}{space 2}        0{col 37}{txt}  (omitted)
{space 10}mv_noreligion {c |}{col 25}{res}{space 2} .1039787{col 37}{space 2} .0433893{col 48}{space 1}    2.40{col 57}{space 3}0.017{col 65}{space 4} .0189325{col 78}{space 3} .1890248
{txt}mv_christiannotcatholic {c |}{col 25}{res}{space 2}        0{col 37}{txt}  (omitted)
{space 12}mv_catholic {c |}{col 25}{res}{space 2}        0{col 37}{txt}  (omitted)
{space 6}mv_fulltimeworker {c |}{col 25}{res}{space 2}        0{col 37}{txt}  (omitted)
{space 6}mv_parttimeworker {c |}{col 25}{res}{space 2}   -.1535{col 37}{space 2} .0927772{col 48}{space 1}   -1.65{col 57}{space 3}0.098{col 65}{space 4}-.3353497{col 78}{space 3} .0283497
{txt}{space 10}mv_unemployed {c |}{col 25}{res}{space 2}        0{col 37}{txt}  (omitted)
{space 8}mv_selfemployed {c |}{col 25}{res}{space 2} .1746695{col 37}{space 2} .0956487{col 48}{space 1}    1.83{col 57}{space 3}0.068{col 65}{space 4}-.0128086{col 78}{space 3} .3621477
{txt}{space 13}mv_outofLF {c |}{col 25}{res}{space 2}        0{col 37}{txt}  (omitted)
{space 10}mv_goodhealth {c |}{col 25}{res}{space 2} .0734923{col 37}{space 2} .1363288{col 48}{space 1}    0.54{col 57}{space 3}0.590{col 65}{space 4}-.1937216{col 78}{space 3} .3407061
{txt}{space 15}mv_white {c |}{col 25}{res}{space 2} .0166993{col 37}{space 2} .1291576{col 48}{space 1}    0.13{col 57}{space 3}0.897{col 65}{space 4}-.2364587{col 78}{space 3} .2698572
{txt}{space 15}mv_black {c |}{col 25}{res}{space 2}        0{col 37}{txt}  (omitted)
{space 14}mv_latino {c |}{col 25}{res}{space 2}        0{col 37}{txt}  (omitted)
{space 15}mv_asian {c |}{col 25}{res}{space 2}        0{col 37}{txt}  (omitted)
{space 9}mv_whitecollar {c |}{col 25}{res}{space 2} .0570981{col 37}{space 2} .0972914{col 48}{space 1}    0.59{col 57}{space 3}0.557{col 65}{space 4}-.1335997{col 78}{space 3}  .247796
{txt}{space 10}mv_bluecollar {c |}{col 25}{res}{space 2}        0{col 37}{txt}  (omitted)
{space 7}mv_serviceworker {c |}{col 25}{res}{space 2}        0{col 37}{txt}  (omitted)
{space 23} {c |}
{space 16}country {c |}
{space 15}Austria  {c |}{col 25}{res}{space 2} .1195001{col 37}{space 2} .1693933{col 48}{space 1}    0.71{col 57}{space 3}0.481{col 65}{space 4}-.2125226{col 78}{space 3} .4515227
{txt}{space 16}Brazil  {c |}{col 25}{res}{space 2}-.1056624{col 37}{space 2} .0292127{col 48}{space 1}   -3.62{col 57}{space 3}0.000{col 65}{space 4}-.1629214{col 78}{space 3}-.0484034
{txt}{space 16}France  {c |}{col 25}{res}{space 2}        0{col 37}{txt}  (omitted)
{space 15}Germany  {c |}{col 25}{res}{space 2} .0692403{col 37}{space 2} .1801542{col 48}{space 1}    0.38{col 57}{space 3}0.701{col 65}{space 4}-.2838745{col 78}{space 3} .4223551
{txt}{space 17}Italy  {c |}{col 25}{res}{space 2}-.1510569{col 37}{space 2} .1672709{col 48}{space 1}   -0.90{col 57}{space 3}0.366{col 65}{space 4}-.4789195{col 78}{space 3} .1768056
{txt}{space 11}New_Zealand  {c |}{col 25}{res}{space 2}        0{col 37}{txt}  (omitted)
{space 16}Poland  {c |}{col 25}{res}{space 2}-.0581811{col 37}{space 2} .0305189{col 48}{space 1}   -1.91{col 57}{space 3}0.057{col 65}{space 4}-.1180003{col 78}{space 3} .0016381
{txt}{space 17}Spain  {c |}{col 25}{res}{space 2}        0{col 37}{txt}  (omitted)
{space 16}Sweden  {c |}{col 25}{res}{space 2} .0819317{col 37}{space 2}  .034285{col 48}{space 1}    2.39{col 57}{space 3}0.017{col 65}{space 4} .0147307{col 78}{space 3} .1491327
{txt}{space 20}UK  {c |}{col 25}{res}{space 2}        0{col 37}{txt}  (omitted)
{space 20}US  {c |}{col 25}{res}{space 2}        0{col 37}{txt}  (omitted)
{space 23} {c |}
{space 18}_cons {c |}{col 25}{res}{space 2}  .118294{col 37}{space 2}  .065827{col 48}{space 1}    1.80{col 57}{space 3}0.072{col 65}{space 4}-.0107315{col 78}{space 3} .2473196
{txt}{hline 24}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{help ivreg2##idtest:Underidentification test} (Kleibergen-Paap rk LM statistic):{res}{col 71}  23.587
{txt}{col 52}Chi-sq({res}15{txt}) P-val =  {res}{col 73}0.0724
{txt}{hline 78}
{help ivreg2##widtest:Weak identification test} (Cragg-Donald Wald F statistic):{res}{col 71}   1.545
{txt}                         (Kleibergen-Paap rk Wald F statistic):{res}{col 71}   1.575
{txt}Stock-Yogo weak ID test critical values:{res}{txt}{col 42} 5% maximal IV relative bias{res}{col 73} 21.23
{txt}{col 42}10% maximal IV relative bias{res}{col 73} 11.51
{txt}{col 42}20% maximal IV relative bias{res}{col 73}  6.42
{txt}{col 42}30% maximal IV relative bias{res}{col 73}  4.63
{txt}{col 42}10% maximal IV size{res}{col 73} 50.39
{txt}{col 42}15% maximal IV size{res}{col 73} 26.80
{txt}{col 42}20% maximal IV size{res}{col 73} 18.72
{txt}{col 42}25% maximal IV size{res}{col 73} 14.60
{txt}Source: Stock-Yogo (2005).  Reproduced by permission.
NB: Critical values are for Cragg-Donald F statistic and i.i.d. errors.
{hline 78}
{help ivreg2##overidtests:Hansen J statistic} (overidentification test of all instruments):{res}{col 71}  11.672
{txt}{col 52}Chi-sq({res}14{txt}) P-val =  {res}{col 73}0.6326
{txt}{hline 78}
Instrumented:{col 23}satis_head
Included instruments:{col 23}thirties fourties fifties sixties seventies
{col 23}income2quartile income3quartile income4quartile
{col 23}incomenoanswer female highschool college noreligion
{col 23}christiannotcatholic catholic fulltimeworker
{col 23}parttimeworker unemployed selfemployed outofLF goodhealth
{col 23}white black latino asian whitecollar bluecollar
{col 23}serviceworker mv_incomenoanswer mv_highschool
{col 23}mv_noreligion mv_parttimeworker mv_selfemployed
{col 23}mv_goodhealth mv_white mv_whitecollar 2.country 3.country
{col 23}5.country 6.country 8.country 10.country
Excluded instruments:{col 23}TH1_TE1 TH1_TE2 TH1_TE3 TH1_TE4 TH2_TE1 TH2_TE2 TH2_TE3
{col 23}TH2_TE4 TH3_TE1 TH3_TE2 TH3_TE3 TH3_TE4 TH4_TE1 TH4_TE2
{col 23}TH4_TE3
Dropped collinear:{col 23}mv_thirties mv_fourties mv_fifties mv_sixties
{col 23}mv_seventies mv_income2quartile mv_income3quartile
{col 23}mv_income4quartile mv_female mv_college
{col 23}mv_christiannotcatholic mv_catholic mv_fulltimeworker
{col 23}mv_unemployed mv_outofLF mv_black mv_latino mv_asian
{col 23}mv_bluecollar mv_serviceworker 4.country 7.country
{col 23}9.country 11.country 12.country
{hline 78}
({res}est1{txt} stored)

{com}. 
. estadd local Controls "X", replace

{txt}added macro:
           e(Controls) : "{res:X}"

{com}. estadd local CFE "X", replace

{txt}added macro:
                e(CFE) : "{res:X}"

{com}. estadd local IV "16 IVs", replace

{txt}added macro:
                 e(IV) : "{res:16 IVs}"

{com}. estadd scalar fstat = e(widstat)

{txt}added scalar:
              e(fstat) =  {res}1.5750517
{txt}
{com}. get_mean

{txt}added scalar:
                 e(av) =  {res}.312
{txt}
{com}. 
. /* E.8. Col 2: External Efficacy with 1 instrument */
. eststo: ivreg2 gov_care_inv ///
>         (satis_head = treatc) $controls $mv_controls  i.country, small robust 
{txt}Warning - collinearities detected
Vars dropped:{col 21}mv_thirties mv_fourties mv_fifties mv_sixties mv_seventies
{col 21}mv_income2quartile mv_income3quartile mv_income4quartile
{col 21}mv_female mv_college mv_christiannotcatholic mv_catholic
{col 21}mv_fulltimeworker mv_unemployed mv_outofLF mv_black
{col 21}mv_latino mv_asian mv_bluecollar mv_serviceworker 4.country
{col 21}7.country 9.country 11.country 12.country
{res}
{txt}IV (2SLS) estimation
{hline 20}

Estimates efficient for homoskedasticity only
Statistics robust to heteroskedasticity

{col 55}Number of obs = {res}   22538
{txt}{col 55}F( 43, 22494) = {res}   26.88
{txt}{col 55}Prob > F      = {res}  0.0000
{txt}Total (centered) SS     = {res} 4833.829843{txt}{col 55}Centered R2   = {res}  0.0958
{txt}Total (uncentered) SS   = {res}        7021{txt}{col 55}Uncentered R2 = {res}  0.3775
{txt}Residual SS             = {res} 4370.630763{txt}{col 55}Root MSE      = {res}   .4408

{txt}{hline 24}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 25}{c |}{col 37}    Robust
{col 1}           gov_care_inv{col 25}{c |} Coefficient{col 37}  std. err.{col 49}      t{col 57}   P>|t|{col 65}     [95% con{col 78}f. interval]
{hline 24}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 13}satis_head {c |}{col 25}{res}{space 2} .1336458{col 37}{space 2}  .469544{col 48}{space 1}    0.28{col 57}{space 3}0.776{col 65}{space 4}-.7866931{col 78}{space 3} 1.053985
{txt}{space 15}thirties {c |}{col 25}{res}{space 2}-.0583922{col 37}{space 2} .0125352{col 48}{space 1}   -4.66{col 57}{space 3}0.000{col 65}{space 4} -.082962{col 78}{space 3}-.0338224
{txt}{space 15}fourties {c |}{col 25}{res}{space 2}-.0527979{col 37}{space 2} .0182876{col 48}{space 1}   -2.89{col 57}{space 3}0.004{col 65}{space 4}-.0886428{col 78}{space 3} -.016953
{txt}{space 16}fifties {c |}{col 25}{res}{space 2}-.0601521{col 37}{space 2}  .019278{col 48}{space 1}   -3.12{col 57}{space 3}0.002{col 65}{space 4}-.0979384{col 78}{space 3}-.0223658
{txt}{space 16}sixties {c |}{col 25}{res}{space 2}-.0872129{col 37}{space 2} .0200477{col 48}{space 1}   -4.35{col 57}{space 3}0.000{col 65}{space 4}-.1265079{col 78}{space 3} -.047918
{txt}{space 14}seventies {c |}{col 25}{res}{space 2} -.043023{col 37}{space 2}  .014745{col 48}{space 1}   -2.92{col 57}{space 3}0.004{col 65}{space 4}-.0719242{col 78}{space 3}-.0141217
{txt}{space 8}income2quartile {c |}{col 25}{res}{space 2} .0092728{col 37}{space 2} .0117824{col 48}{space 1}    0.79{col 57}{space 3}0.431{col 65}{space 4}-.0138216{col 78}{space 3} .0323672
{txt}{space 8}income3quartile {c |}{col 25}{res}{space 2} .0036095{col 37}{space 2} .0138109{col 48}{space 1}    0.26{col 57}{space 3}0.794{col 65}{space 4}-.0234608{col 78}{space 3} .0306798
{txt}{space 8}income4quartile {c |}{col 25}{res}{space 2} .0529806{col 37}{space 2} .0214518{col 48}{space 1}    2.47{col 57}{space 3}0.014{col 65}{space 4} .0109336{col 78}{space 3} .0950276
{txt}{space 9}incomenoanswer {c |}{col 25}{res}{space 2} .0163612{col 37}{space 2} .0150435{col 48}{space 1}    1.09{col 57}{space 3}0.277{col 65}{space 4}-.0131252{col 78}{space 3} .0458475
{txt}{space 17}female {c |}{col 25}{res}{space 2}-.0006059{col 37}{space 2} .0078449{col 48}{space 1}   -0.08{col 57}{space 3}0.938{col 65}{space 4}-.0159825{col 78}{space 3} .0147707
{txt}{space 13}highschool {c |}{col 25}{res}{space 2} .0035543{col 37}{space 2} .0107407{col 48}{space 1}    0.33{col 57}{space 3}0.741{col 65}{space 4}-.0174982{col 78}{space 3} .0246068
{txt}{space 16}college {c |}{col 25}{res}{space 2}  .032911{col 37}{space 2} .0093521{col 48}{space 1}    3.52{col 57}{space 3}0.000{col 65}{space 4} .0145803{col 78}{space 3} .0512417
{txt}{space 13}noreligion {c |}{col 25}{res}{space 2}-.0307141{col 37}{space 2} .0194459{col 48}{space 1}   -1.58{col 57}{space 3}0.114{col 65}{space 4}-.0688294{col 78}{space 3} .0074013
{txt}{space 3}christiannotcatholic {c |}{col 25}{res}{space 2} .0135672{col 37}{space 2} .0333528{col 48}{space 1}    0.41{col 57}{space 3}0.684{col 65}{space 4}-.0518067{col 78}{space 3} .0789411
{txt}{space 15}catholic {c |}{col 25}{res}{space 2}-.0139393{col 37}{space 2} .0163999{col 48}{space 1}   -0.85{col 57}{space 3}0.395{col 65}{space 4}-.0460843{col 78}{space 3} .0182057
{txt}{space 9}fulltimeworker {c |}{col 25}{res}{space 2}-.2110517{col 37}{space 2} .1477095{col 48}{space 1}   -1.43{col 57}{space 3}0.153{col 65}{space 4}-.5005726{col 78}{space 3} .0784692
{txt}{space 9}parttimeworker {c |}{col 25}{res}{space 2}-.1499931{col 37}{space 2} .1483912{col 48}{space 1}   -1.01{col 57}{space 3}0.312{col 65}{space 4}-.4408502{col 78}{space 3} .1408641
{txt}{space 13}unemployed {c |}{col 25}{res}{space 2}-.2297081{col 37}{space 2} .1642677{col 48}{space 1}   -1.40{col 57}{space 3}0.162{col 65}{space 4}-.5516842{col 78}{space 3}  .092268
{txt}{space 11}selfemployed {c |}{col 25}{res}{space 2}-.2212688{col 37}{space 2}  .180333{col 48}{space 1}   -1.23{col 57}{space 3}0.220{col 65}{space 4} -.574734{col 78}{space 3} .1321963
{txt}{space 16}outofLF {c |}{col 25}{res}{space 2}  -.19227{col 37}{space 2} .1533282{col 48}{space 1}   -1.25{col 57}{space 3}0.210{col 65}{space 4} -.492804{col 78}{space 3}  .108264
{txt}{space 13}goodhealth {c |}{col 25}{res}{space 2} .0232612{col 37}{space 2} .0243046{col 48}{space 1}    0.96{col 57}{space 3}0.339{col 65}{space 4}-.0243776{col 78}{space 3} .0708999
{txt}{space 18}white {c |}{col 25}{res}{space 2}-.0267565{col 37}{space 2} .0845733{col 48}{space 1}   -0.32{col 57}{space 3}0.752{col 65}{space 4}-.1925259{col 78}{space 3}  .139013
{txt}{space 18}black {c |}{col 25}{res}{space 2} .0400356{col 37}{space 2} .0649829{col 48}{space 1}    0.62{col 57}{space 3}0.538{col 65}{space 4}-.0873355{col 78}{space 3} .1674067
{txt}{space 17}latino {c |}{col 25}{res}{space 2} .0789617{col 37}{space 2}  .074828{col 48}{space 1}    1.06{col 57}{space 3}0.291{col 65}{space 4}-.0677064{col 78}{space 3} .2256299
{txt}{space 18}asian {c |}{col 25}{res}{space 2}  .112214{col 37}{space 2} .0804024{col 48}{space 1}    1.40{col 57}{space 3}0.163{col 65}{space 4}-.0453802{col 78}{space 3} .2698083
{txt}{space 12}whitecollar {c |}{col 25}{res}{space 2}-.0066913{col 37}{space 2} .0175832{col 48}{space 1}   -0.38{col 57}{space 3}0.704{col 65}{space 4}-.0411557{col 78}{space 3}  .027773
{txt}{space 13}bluecollar {c |}{col 25}{res}{space 2} -.085003{col 37}{space 2} .0189175{col 48}{space 1}   -4.49{col 57}{space 3}0.000{col 65}{space 4}-.1220827{col 78}{space 3}-.0479234
{txt}{space 10}serviceworker {c |}{col 25}{res}{space 2}-.0173053{col 37}{space 2}  .013781{col 48}{space 1}   -1.26{col 57}{space 3}0.209{col 65}{space 4} -.044317{col 78}{space 3} .0097064
{txt}{space 12}mv_thirties {c |}{col 25}{res}{space 2}        0{col 37}{txt}  (omitted)
{space 12}mv_fourties {c |}{col 25}{res}{space 2}        0{col 37}{txt}  (omitted)
{space 13}mv_fifties {c |}{col 25}{res}{space 2}        0{col 37}{txt}  (omitted)
{space 13}mv_sixties {c |}{col 25}{res}{space 2}        0{col 37}{txt}  (omitted)
{space 11}mv_seventies {c |}{col 25}{res}{space 2}        0{col 37}{txt}  (omitted)
{space 5}mv_income2quartile {c |}{col 25}{res}{space 2}        0{col 37}{txt}  (omitted)
{space 5}mv_income3quartile {c |}{col 25}{res}{space 2}        0{col 37}{txt}  (omitted)
{space 5}mv_income4quartile {c |}{col 25}{res}{space 2}        0{col 37}{txt}  (omitted)
{space 6}mv_incomenoanswer {c |}{col 25}{res}{space 2}-.0663466{col 37}{space 2} .0412631{col 48}{space 1}   -1.61{col 57}{space 3}0.108{col 65}{space 4}-.1472251{col 78}{space 3} .0145319
{txt}{space 14}mv_female {c |}{col 25}{res}{space 2}        0{col 37}{txt}  (omitted)
{space 10}mv_highschool {c |}{col 25}{res}{space 2} .0208468{col 37}{space 2} .0240691{col 48}{space 1}    0.87{col 57}{space 3}0.386{col 65}{space 4}-.0263303{col 78}{space 3}  .068024
{txt}{space 13}mv_college {c |}{col 25}{res}{space 2}        0{col 37}{txt}  (omitted)
{space 10}mv_noreligion {c |}{col 25}{res}{space 2} .1054734{col 37}{space 2} .0455428{col 48}{space 1}    2.32{col 57}{space 3}0.021{col 65}{space 4} .0162063{col 78}{space 3} .1947406
{txt}mv_christiannotcatholic {c |}{col 25}{res}{space 2}        0{col 37}{txt}  (omitted)
{space 12}mv_catholic {c |}{col 25}{res}{space 2}        0{col 37}{txt}  (omitted)
{space 6}mv_fulltimeworker {c |}{col 25}{res}{space 2}        0{col 37}{txt}  (omitted)
{space 6}mv_parttimeworker {c |}{col 25}{res}{space 2}-.2474021{col 37}{space 2} .1386151{col 48}{space 1}   -1.78{col 57}{space 3}0.074{col 65}{space 4}-.5190973{col 78}{space 3} .0242931
{txt}{space 10}mv_unemployed {c |}{col 25}{res}{space 2}        0{col 37}{txt}  (omitted)
{space 8}mv_selfemployed {c |}{col 25}{res}{space 2} .2710493{col 37}{space 2} .1424919{col 48}{space 1}    1.90{col 57}{space 3}0.057{col 65}{space 4}-.0082447{col 78}{space 3} .5503434
{txt}{space 13}mv_outofLF {c |}{col 25}{res}{space 2}        0{col 37}{txt}  (omitted)
{space 10}mv_goodhealth {c |}{col 25}{res}{space 2} .1311687{col 37}{space 2} .1555096{col 48}{space 1}    0.84{col 57}{space 3}0.399{col 65}{space 4}-.1736409{col 78}{space 3} .4359784
{txt}{space 15}mv_white {c |}{col 25}{res}{space 2} .1404347{col 37}{space 2} .1882597{col 48}{space 1}    0.75{col 57}{space 3}0.456{col 65}{space 4}-.2285673{col 78}{space 3} .5094367
{txt}{space 15}mv_black {c |}{col 25}{res}{space 2}        0{col 37}{txt}  (omitted)
{space 14}mv_latino {c |}{col 25}{res}{space 2}        0{col 37}{txt}  (omitted)
{space 15}mv_asian {c |}{col 25}{res}{space 2}        0{col 37}{txt}  (omitted)
{space 9}mv_whitecollar {c |}{col 25}{res}{space 2}  .155806{col 37}{space 2} .1458461{col 48}{space 1}    1.07{col 57}{space 3}0.285{col 65}{space 4}-.1300624{col 78}{space 3} .4416744
{txt}{space 10}mv_bluecollar {c |}{col 25}{res}{space 2}        0{col 37}{txt}  (omitted)
{space 7}mv_serviceworker {c |}{col 25}{res}{space 2}        0{col 37}{txt}  (omitted)
{space 23} {c |}
{space 16}country {c |}
{space 15}Austria  {c |}{col 25}{res}{space 2}  .287037{col 37}{space 2} .2507702{col 48}{space 1}    1.14{col 57}{space 3}0.252{col 65}{space 4}  -.20449{col 78}{space 3} .7785639
{txt}{space 16}Brazil  {c |}{col 25}{res}{space 2} -.110796{col 37}{space 2} .0308346{col 48}{space 1}   -3.59{col 57}{space 3}0.000{col 65}{space 4}-.1712338{col 78}{space 3}-.0503581
{txt}{space 16}France  {c |}{col 25}{res}{space 2}        0{col 37}{txt}  (omitted)
{space 15}Germany  {c |}{col 25}{res}{space 2} .2494017{col 37}{space 2} .2681867{col 48}{space 1}    0.93{col 57}{space 3}0.352{col 65}{space 4}-.2762629{col 78}{space 3} .7750662
{txt}{space 17}Italy  {c |}{col 25}{res}{space 2} .0081992{col 37}{space 2} .2428477{col 48}{space 1}    0.03{col 57}{space 3}0.973{col 65}{space 4}-.4677992{col 78}{space 3} .4841976
{txt}{space 11}New_Zealand  {c |}{col 25}{res}{space 2}        0{col 37}{txt}  (omitted)
{space 16}Poland  {c |}{col 25}{res}{space 2}-.0692649{col 37}{space 2} .0342174{col 48}{space 1}   -2.02{col 57}{space 3}0.043{col 65}{space 4}-.1363334{col 78}{space 3}-.0021964
{txt}{space 17}Spain  {c |}{col 25}{res}{space 2}        0{col 37}{txt}  (omitted)
{space 16}Sweden  {c |}{col 25}{res}{space 2} .1010597{col 37}{space 2} .0411856{col 48}{space 1}    2.45{col 57}{space 3}0.014{col 65}{space 4}  .020333{col 78}{space 3} .1817864
{txt}{space 20}UK  {c |}{col 25}{res}{space 2}        0{col 37}{txt}  (omitted)
{space 20}US  {c |}{col 25}{res}{space 2}        0{col 37}{txt}  (omitted)
{space 23} {c |}
{space 18}_cons {c |}{col 25}{res}{space 2} .1156315{col 37}{space 2} .0679247{col 48}{space 1}    1.70{col 57}{space 3}0.089{col 65}{space 4}-.0175057{col 78}{space 3} .2487687
{txt}{hline 24}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{help ivreg2##idtest:Underidentification test} (Kleibergen-Paap rk LM statistic):{res}{col 71}  10.218
{txt}{col 52}Chi-sq({res}1{txt}) P-val =  {res}{col 73}0.0014
{txt}{hline 78}
{help ivreg2##widtest:Weak identification test} (Cragg-Donald Wald F statistic):{res}{col 71}  10.187
{txt}                         (Kleibergen-Paap rk Wald F statistic):{res}{col 71}  10.214
{txt}Stock-Yogo weak ID test critical values:{res}{txt}{col 42}10% maximal IV size{res}{col 73} 16.38
{txt}{col 42}15% maximal IV size{res}{col 73}  8.96
{txt}{col 42}20% maximal IV size{res}{col 73}  6.66
{txt}{col 42}25% maximal IV size{res}{col 73}  5.53
{txt}Source: Stock-Yogo (2005).  Reproduced by permission.
NB: Critical values are for Cragg-Donald F statistic and i.i.d. errors.
{hline 78}
{help ivreg2##overidtests:Hansen J statistic} (overidentification test of all instruments):{res}{col 71}   0.000
{txt}{col 50}(equation exactly identified)
{hline 78}
Instrumented:{col 23}satis_head
Included instruments:{col 23}thirties fourties fifties sixties seventies
{col 23}income2quartile income3quartile income4quartile
{col 23}incomenoanswer female highschool college noreligion
{col 23}christiannotcatholic catholic fulltimeworker
{col 23}parttimeworker unemployed selfemployed outofLF goodhealth
{col 23}white black latino asian whitecollar bluecollar
{col 23}serviceworker mv_incomenoanswer mv_highschool
{col 23}mv_noreligion mv_parttimeworker mv_selfemployed
{col 23}mv_goodhealth mv_white mv_whitecollar 2.country 3.country
{col 23}5.country 6.country 8.country 10.country
Excluded instruments:{col 23}treatc
Dropped collinear:{col 23}mv_thirties mv_fourties mv_fifties mv_sixties
{col 23}mv_seventies mv_income2quartile mv_income3quartile
{col 23}mv_income4quartile mv_female mv_college
{col 23}mv_christiannotcatholic mv_catholic mv_fulltimeworker
{col 23}mv_unemployed mv_outofLF mv_black mv_latino mv_asian
{col 23}mv_bluecollar mv_serviceworker 4.country 7.country
{col 23}9.country 11.country 12.country
{hline 78}
({res}est2{txt} stored)

{com}. 
. estadd local IV "SumIV", replace

{txt}added macro:
                 e(IV) : "{res:SumIV}"

{com}. estadd scalar fstat = e(widstat)

{txt}added scalar:
              e(fstat) =  {res}10.213956
{txt}
{com}. estadd local Controls "X", replace

{txt}added macro:
           e(Controls) : "{res:X}"

{com}. estadd local CFE "X", replace

{txt}added macro:
                e(CFE) : "{res:X}"

{com}. get_mean

{txt}added scalar:
                 e(av) =  {res}.312
{txt}
{com}. 
. /* E.8. Col 3: Internal Efficacy with 16 instruments */
. eststo: ivreg2 complicated_inv ///
>         (satis_head = TH1_TE1 TH1_TE2 TH1_TE3 TH1_TE4 TH2_TE1 TH2_TE2 TH2_TE3 TH2_TE4 TH3_TE1 TH3_TE2 TH3_TE3 TH3_TE4 TH4_TE1 TH4_TE2 TH4_TE3) $controls $mv_controls  i.country, small robust 
{txt}Warning - collinearities detected
Vars dropped:{col 21}mv_thirties mv_fourties mv_fifties mv_sixties mv_seventies
{col 21}mv_income2quartile mv_income3quartile mv_income4quartile
{col 21}mv_female mv_college mv_christiannotcatholic mv_catholic
{col 21}mv_fulltimeworker mv_unemployed mv_outofLF mv_black
{col 21}mv_latino mv_asian mv_bluecollar mv_serviceworker 4.country
{col 21}7.country 9.country 11.country 12.country
{res}
{txt}IV (2SLS) estimation
{hline 20}

Estimates efficient for homoskedasticity only
Statistics robust to heteroskedasticity

{col 55}Number of obs = {res}   22539
{txt}{col 55}F( 43, 22495) = {res}   21.62
{txt}{col 55}Prob > F      = {res}  0.0000
{txt}Total (centered) SS     = {res} 5461.776476{txt}{col 55}Centered R2   = {res}  0.0284
{txt}Total (uncentered) SS   = {res}       13244{txt}{col 55}Uncentered R2 = {res}  0.5993
{txt}Residual SS             = {res} 5306.614814{txt}{col 55}Root MSE      = {res}   .4857

{txt}{hline 24}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 25}{c |}{col 37}    Robust
{col 1}        complicated_inv{col 25}{c |} Coefficient{col 37}  std. err.{col 49}      t{col 57}   P>|t|{col 65}     [95% con{col 78}f. interval]
{hline 24}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 13}satis_head {c |}{col 25}{res}{space 2} .0725069{col 37}{space 2} .3428094{col 48}{space 1}    0.21{col 57}{space 3}0.832{col 65}{space 4}-.5994234{col 78}{space 3} .7444371
{txt}{space 15}thirties {c |}{col 25}{res}{space 2} .0311177{col 37}{space 2}  .012739{col 48}{space 1}    2.44{col 57}{space 3}0.015{col 65}{space 4} .0061484{col 78}{space 3}  .056087
{txt}{space 15}fourties {c |}{col 25}{res}{space 2} .0761723{col 37}{space 2} .0158803{col 48}{space 1}    4.80{col 57}{space 3}0.000{col 65}{space 4} .0450458{col 78}{space 3} .1072988
{txt}{space 16}fifties {c |}{col 25}{res}{space 2} .1106029{col 37}{space 2}  .016618{col 48}{space 1}    6.66{col 57}{space 3}0.000{col 65}{space 4} .0780305{col 78}{space 3} .1431754
{txt}{space 16}sixties {c |}{col 25}{res}{space 2}  .121405{col 37}{space 2} .0170779{col 48}{space 1}    7.11{col 57}{space 3}0.000{col 65}{space 4} .0879311{col 78}{space 3} .1548788
{txt}{space 14}seventies {c |}{col 25}{res}{space 2} .1256583{col 37}{space 2} .0148579{col 48}{space 1}    8.46{col 57}{space 3}0.000{col 65}{space 4} .0965357{col 78}{space 3} .1547808
{txt}{space 8}income2quartile {c |}{col 25}{res}{space 2}-.0075378{col 37}{space 2} .0112965{col 48}{space 1}   -0.67{col 57}{space 3}0.505{col 65}{space 4}-.0296797{col 78}{space 3} .0146042
{txt}{space 8}income3quartile {c |}{col 25}{res}{space 2}-.0042428{col 37}{space 2} .0126132{col 48}{space 1}   -0.34{col 57}{space 3}0.737{col 65}{space 4}-.0289655{col 78}{space 3} .0204799
{txt}{space 8}income4quartile {c |}{col 25}{res}{space 2}  .045178{col 37}{space 2} .0173393{col 48}{space 1}    2.61{col 57}{space 3}0.009{col 65}{space 4} .0111918{col 78}{space 3} .0791642
{txt}{space 9}incomenoanswer {c |}{col 25}{res}{space 2}  .045443{col 37}{space 2}   .01565{col 48}{space 1}    2.90{col 57}{space 3}0.004{col 65}{space 4} .0147679{col 78}{space 3} .0761181
{txt}{space 17}female {c |}{col 25}{res}{space 2}-.0531177{col 37}{space 2} .0075303{col 48}{space 1}   -7.05{col 57}{space 3}0.000{col 65}{space 4}-.0678776{col 78}{space 3}-.0383578
{txt}{space 13}highschool {c |}{col 25}{res}{space 2} .0708975{col 37}{space 2}  .011771{col 48}{space 1}    6.02{col 57}{space 3}0.000{col 65}{space 4} .0478254{col 78}{space 3} .0939695
{txt}{space 16}college {c |}{col 25}{res}{space 2} .1439814{col 37}{space 2}  .010587{col 48}{space 1}   13.60{col 57}{space 3}0.000{col 65}{space 4} .1232303{col 78}{space 3} .1647326
{txt}{space 13}noreligion {c |}{col 25}{res}{space 2} .0592901{col 37}{space 2} .0176126{col 48}{space 1}    3.37{col 57}{space 3}0.001{col 65}{space 4} .0247682{col 78}{space 3}  .093812
{txt}{space 3}christiannotcatholic {c |}{col 25}{res}{space 2} .0009184{col 37}{space 2} .0270261{col 48}{space 1}    0.03{col 57}{space 3}0.973{col 65}{space 4}-.0520546{col 78}{space 3} .0538914
{txt}{space 15}catholic {c |}{col 25}{res}{space 2}-.0062994{col 37}{space 2} .0161226{col 48}{space 1}   -0.39{col 57}{space 3}0.696{col 65}{space 4}-.0379009{col 78}{space 3}  .025302
{txt}{space 9}fulltimeworker {c |}{col 25}{res}{space 2} .0898521{col 37}{space 2} .1087358{col 48}{space 1}    0.83{col 57}{space 3}0.409{col 65}{space 4}-.1232775{col 78}{space 3} .3029818
{txt}{space 9}parttimeworker {c |}{col 25}{res}{space 2} .1722545{col 37}{space 2} .1121356{col 48}{space 1}    1.54{col 57}{space 3}0.125{col 65}{space 4} -.047539{col 78}{space 3}  .392048
{txt}{space 13}unemployed {c |}{col 25}{res}{space 2} .1156152{col 37}{space 2} .1216157{col 48}{space 1}    0.95{col 57}{space 3}0.342{col 65}{space 4}  -.12276{col 78}{space 3} .3539903
{txt}{space 11}selfemployed {c |}{col 25}{res}{space 2} .0734308{col 37}{space 2} .1399884{col 48}{space 1}    0.52{col 57}{space 3}0.600{col 65}{space 4}-.2009561{col 78}{space 3} .3478177
{txt}{space 16}outofLF {c |}{col 25}{res}{space 2}  .072048{col 37}{space 2} .1129948{col 48}{space 1}    0.64{col 57}{space 3}0.524{col 65}{space 4}-.1494297{col 78}{space 3} .2935258
{txt}{space 13}goodhealth {c |}{col 25}{res}{space 2}-.0034337{col 37}{space 2} .0185412{col 48}{space 1}   -0.19{col 57}{space 3}0.853{col 65}{space 4}-.0397757{col 78}{space 3} .0329083
{txt}{space 18}white {c |}{col 25}{res}{space 2}-.1386654{col 37}{space 2}  .077555{col 48}{space 1}   -1.79{col 57}{space 3}0.074{col 65}{space 4}-.2906786{col 78}{space 3} .0133479
{txt}{space 18}black {c |}{col 25}{res}{space 2}-.0754726{col 37}{space 2} .0704738{col 48}{space 1}   -1.07{col 57}{space 3}0.284{col 65}{space 4}-.2136061{col 78}{space 3} .0626609
{txt}{space 17}latino {c |}{col 25}{res}{space 2}-.0840988{col 37}{space 2} .0790607{col 48}{space 1}   -1.06{col 57}{space 3}0.287{col 65}{space 4}-.2390633{col 78}{space 3} .0708656
{txt}{space 18}asian {c |}{col 25}{res}{space 2}-.1571735{col 37}{space 2} .0811394{col 48}{space 1}   -1.94{col 57}{space 3}0.053{col 65}{space 4}-.3162124{col 78}{space 3} .0018655
{txt}{space 12}whitecollar {c |}{col 25}{res}{space 2} .0194574{col 37}{space 2} .0173579{col 48}{space 1}    1.12{col 57}{space 3}0.262{col 65}{space 4}-.0145653{col 78}{space 3}   .05348
{txt}{space 13}bluecollar {c |}{col 25}{res}{space 2}-.0531045{col 37}{space 2} .0193381{col 48}{space 1}   -2.75{col 57}{space 3}0.006{col 65}{space 4}-.0910086{col 78}{space 3}-.0152004
{txt}{space 10}serviceworker {c |}{col 25}{res}{space 2}-.0240381{col 37}{space 2} .0143349{col 48}{space 1}   -1.68{col 57}{space 3}0.094{col 65}{space 4}-.0521356{col 78}{space 3} .0040594
{txt}{space 12}mv_thirties {c |}{col 25}{res}{space 2}        0{col 37}{txt}  (omitted)
{space 12}mv_fourties {c |}{col 25}{res}{space 2}        0{col 37}{txt}  (omitted)
{space 13}mv_fifties {c |}{col 25}{res}{space 2}        0{col 37}{txt}  (omitted)
{space 13}mv_sixties {c |}{col 25}{res}{space 2}        0{col 37}{txt}  (omitted)
{space 11}mv_seventies {c |}{col 25}{res}{space 2}        0{col 37}{txt}  (omitted)
{space 5}mv_income2quartile {c |}{col 25}{res}{space 2}        0{col 37}{txt}  (omitted)
{space 5}mv_income3quartile {c |}{col 25}{res}{space 2}        0{col 37}{txt}  (omitted)
{space 5}mv_income4quartile {c |}{col 25}{res}{space 2}        0{col 37}{txt}  (omitted)
{space 6}mv_incomenoanswer {c |}{col 25}{res}{space 2}-.0039549{col 37}{space 2} .0342804{col 48}{space 1}   -0.12{col 57}{space 3}0.908{col 65}{space 4}-.0711469{col 78}{space 3} .0632371
{txt}{space 14}mv_female {c |}{col 25}{res}{space 2}        0{col 37}{txt}  (omitted)
{space 10}mv_highschool {c |}{col 25}{res}{space 2} .0753763{col 37}{space 2} .0276225{col 48}{space 1}    2.73{col 57}{space 3}0.006{col 65}{space 4} .0212342{col 78}{space 3} .1295183
{txt}{space 13}mv_college {c |}{col 25}{res}{space 2}        0{col 37}{txt}  (omitted)
{space 10}mv_noreligion {c |}{col 25}{res}{space 2} .0618891{col 37}{space 2} .0463303{col 48}{space 1}    1.34{col 57}{space 3}0.182{col 65}{space 4}-.0289215{col 78}{space 3} .1526997
{txt}mv_christiannotcatholic {c |}{col 25}{res}{space 2}        0{col 37}{txt}  (omitted)
{space 12}mv_catholic {c |}{col 25}{res}{space 2}        0{col 37}{txt}  (omitted)
{space 6}mv_fulltimeworker {c |}{col 25}{res}{space 2}        0{col 37}{txt}  (omitted)
{space 6}mv_parttimeworker {c |}{col 25}{res}{space 2}   .03942{col 37}{space 2} .1042713{col 48}{space 1}    0.38{col 57}{space 3}0.705{col 65}{space 4} -.164959{col 78}{space 3}  .243799
{txt}{space 10}mv_unemployed {c |}{col 25}{res}{space 2}        0{col 37}{txt}  (omitted)
{space 8}mv_selfemployed {c |}{col 25}{res}{space 2}-.0621496{col 37}{space 2} .1073517{col 48}{space 1}   -0.58{col 57}{space 3}0.563{col 65}{space 4}-.2725665{col 78}{space 3} .1482672
{txt}{space 13}mv_outofLF {c |}{col 25}{res}{space 2}        0{col 37}{txt}  (omitted)
{space 10}mv_goodhealth {c |}{col 25}{res}{space 2}-.0880282{col 37}{space 2} .1559816{col 48}{space 1}   -0.56{col 57}{space 3}0.573{col 65}{space 4} -.393763{col 78}{space 3} .2177066
{txt}{space 15}mv_white {c |}{col 25}{res}{space 2} -.168449{col 37}{space 2} .1454428{col 48}{space 1}   -1.16{col 57}{space 3}0.247{col 65}{space 4} -.453527{col 78}{space 3}  .116629
{txt}{space 15}mv_black {c |}{col 25}{res}{space 2}        0{col 37}{txt}  (omitted)
{space 14}mv_latino {c |}{col 25}{res}{space 2}        0{col 37}{txt}  (omitted)
{space 15}mv_asian {c |}{col 25}{res}{space 2}        0{col 37}{txt}  (omitted)
{space 9}mv_whitecollar {c |}{col 25}{res}{space 2}-.0817979{col 37}{space 2} .1101722{col 48}{space 1}   -0.74{col 57}{space 3}0.458{col 65}{space 4}-.2977431{col 78}{space 3} .1341473
{txt}{space 10}mv_bluecollar {c |}{col 25}{res}{space 2}        0{col 37}{txt}  (omitted)
{space 7}mv_serviceworker {c |}{col 25}{res}{space 2}        0{col 37}{txt}  (omitted)
{space 23} {c |}
{space 16}country {c |}
{space 15}Austria  {c |}{col 25}{res}{space 2}-.0944969{col 37}{space 2} .1911913{col 48}{space 1}   -0.49{col 57}{space 3}0.621{col 65}{space 4}-.4692452{col 78}{space 3} .2802514
{txt}{space 16}Brazil  {c |}{col 25}{res}{space 2}-.1480094{col 37}{space 2} .0350479{col 48}{space 1}   -4.22{col 57}{space 3}0.000{col 65}{space 4}-.2167057{col 78}{space 3}-.0793131
{txt}{space 16}France  {c |}{col 25}{res}{space 2}        0{col 37}{txt}  (omitted)
{space 15}Germany  {c |}{col 25}{res}{space 2}-.1535514{col 37}{space 2} .2032982{col 48}{space 1}   -0.76{col 57}{space 3}0.450{col 65}{space 4}-.5520301{col 78}{space 3} .2449273
{txt}{space 17}Italy  {c |}{col 25}{res}{space 2}-.2733017{col 37}{space 2} .1884377{col 48}{space 1}   -1.45{col 57}{space 3}0.147{col 65}{space 4}-.6426527{col 78}{space 3} .0960492
{txt}{space 11}New_Zealand  {c |}{col 25}{res}{space 2}        0{col 37}{txt}  (omitted)
{space 16}Poland  {c |}{col 25}{res}{space 2}-.0028223{col 37}{space 2} .0368497{col 48}{space 1}   -0.08{col 57}{space 3}0.939{col 65}{space 4}-.0750503{col 78}{space 3} .0694057
{txt}{space 17}Spain  {c |}{col 25}{res}{space 2}        0{col 37}{txt}  (omitted)
{space 16}Sweden  {c |}{col 25}{res}{space 2}-.1204403{col 37}{space 2} .0399525{col 48}{space 1}   -3.01{col 57}{space 3}0.003{col 65}{space 4}-.1987501{col 78}{space 3}-.0421306
{txt}{space 20}UK  {c |}{col 25}{res}{space 2}        0{col 37}{txt}  (omitted)
{space 20}US  {c |}{col 25}{res}{space 2}        0{col 37}{txt}  (omitted)
{space 23} {c |}
{space 18}_cons {c |}{col 25}{res}{space 2} .5925334{col 37}{space 2} .0744953{col 48}{space 1}    7.95{col 57}{space 3}0.000{col 65}{space 4} .4465175{col 78}{space 3} .7385493
{txt}{hline 24}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{help ivreg2##idtest:Underidentification test} (Kleibergen-Paap rk LM statistic):{res}{col 71}  23.354
{txt}{col 52}Chi-sq({res}15{txt}) P-val =  {res}{col 73}0.0769
{txt}{hline 78}
{help ivreg2##widtest:Weak identification test} (Cragg-Donald Wald F statistic):{res}{col 71}   1.529
{txt}                         (Kleibergen-Paap rk Wald F statistic):{res}{col 71}   1.559
{txt}Stock-Yogo weak ID test critical values:{res}{txt}{col 42} 5% maximal IV relative bias{res}{col 73} 21.23
{txt}{col 42}10% maximal IV relative bias{res}{col 73} 11.51
{txt}{col 42}20% maximal IV relative bias{res}{col 73}  6.42
{txt}{col 42}30% maximal IV relative bias{res}{col 73}  4.63
{txt}{col 42}10% maximal IV size{res}{col 73} 50.39
{txt}{col 42}15% maximal IV size{res}{col 73} 26.80
{txt}{col 42}20% maximal IV size{res}{col 73} 18.72
{txt}{col 42}25% maximal IV size{res}{col 73} 14.60
{txt}Source: Stock-Yogo (2005).  Reproduced by permission.
NB: Critical values are for Cragg-Donald F statistic and i.i.d. errors.
{hline 78}
{help ivreg2##overidtests:Hansen J statistic} (overidentification test of all instruments):{res}{col 71}  19.903
{txt}{col 52}Chi-sq({res}14{txt}) P-val =  {res}{col 73}0.1332
{txt}{hline 78}
Instrumented:{col 23}satis_head
Included instruments:{col 23}thirties fourties fifties sixties seventies
{col 23}income2quartile income3quartile income4quartile
{col 23}incomenoanswer female highschool college noreligion
{col 23}christiannotcatholic catholic fulltimeworker
{col 23}parttimeworker unemployed selfemployed outofLF goodhealth
{col 23}white black latino asian whitecollar bluecollar
{col 23}serviceworker mv_incomenoanswer mv_highschool
{col 23}mv_noreligion mv_parttimeworker mv_selfemployed
{col 23}mv_goodhealth mv_white mv_whitecollar 2.country 3.country
{col 23}5.country 6.country 8.country 10.country
Excluded instruments:{col 23}TH1_TE1 TH1_TE2 TH1_TE3 TH1_TE4 TH2_TE1 TH2_TE2 TH2_TE3
{col 23}TH2_TE4 TH3_TE1 TH3_TE2 TH3_TE3 TH3_TE4 TH4_TE1 TH4_TE2
{col 23}TH4_TE3
Dropped collinear:{col 23}mv_thirties mv_fourties mv_fifties mv_sixties
{col 23}mv_seventies mv_income2quartile mv_income3quartile
{col 23}mv_income4quartile mv_female mv_college
{col 23}mv_christiannotcatholic mv_catholic mv_fulltimeworker
{col 23}mv_unemployed mv_outofLF mv_black mv_latino mv_asian
{col 23}mv_bluecollar mv_serviceworker 4.country 7.country
{col 23}9.country 11.country 12.country
{hline 78}
({res}est3{txt} stored)

{com}. 
. estadd scalar fstat = e(widstat)

{txt}added scalar:
              e(fstat) =  {res}1.5594678
{txt}
{com}. estadd local IV "16 IVs", replace

{txt}added macro:
                 e(IV) : "{res:16 IVs}"

{com}. estadd local Controls "X", replace

{txt}added macro:
           e(Controls) : "{res:X}"

{com}. estadd local CFE "X", replace

{txt}added macro:
                e(CFE) : "{res:X}"

{com}. get_mean

{txt}added scalar:
                 e(av) =  {res}.588
{txt}
{com}. 
. /* E.8. Col 4: Internal Efficacy with 1 instrument */
. eststo: ivreg2 complicated_inv ///
>         (satis_head = treatc ) $controls $mv_controls  i.country, small robust 
{txt}Warning - collinearities detected
Vars dropped:{col 21}mv_thirties mv_fourties mv_fifties mv_sixties mv_seventies
{col 21}mv_income2quartile mv_income3quartile mv_income4quartile
{col 21}mv_female mv_college mv_christiannotcatholic mv_catholic
{col 21}mv_fulltimeworker mv_unemployed mv_outofLF mv_black
{col 21}mv_latino mv_asian mv_bluecollar mv_serviceworker 4.country
{col 21}7.country 9.country 11.country 12.country
{res}
{txt}IV (2SLS) estimation
{hline 20}

Estimates efficient for homoskedasticity only
Statistics robust to heteroskedasticity

{col 55}Number of obs = {res}   22539
{txt}{col 55}F( 43, 22495) = {res}   18.84
{txt}{col 55}Prob > F      = {res}  0.0000
{txt}Total (centered) SS     = {res} 5461.776476{txt}{col 55}Centered R2   = {res} -0.1184
{txt}Total (uncentered) SS   = {res}       13244{txt}{col 55}Uncentered R2 = {res}  0.5388
{txt}Residual SS             = {res} 6108.558302{txt}{col 55}Root MSE      = {res}   .5211

{txt}{hline 24}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 25}{c |}{col 37}    Robust
{col 1}        complicated_inv{col 25}{c |} Coefficient{col 37}  std. err.{col 49}      t{col 57}   P>|t|{col 65}     [95% con{col 78}f. interval]
{hline 24}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 13}satis_head {c |}{col 25}{res}{space 2}  .538665{col 37}{space 2} .5583454{col 48}{space 1}    0.96{col 57}{space 3}0.335{col 65}{space 4}-.5557307{col 78}{space 3} 1.633061
{txt}{space 15}thirties {c |}{col 25}{res}{space 2} .0373042{col 37}{space 2} .0147839{col 48}{space 1}    2.52{col 57}{space 3}0.012{col 65}{space 4} .0083267{col 78}{space 3} .0662816
{txt}{space 15}fourties {c |}{col 25}{res}{space 2} .0908983{col 37}{space 2} .0216239{col 48}{space 1}    4.20{col 57}{space 3}0.000{col 65}{space 4}  .048514{col 78}{space 3} .1332826
{txt}{space 16}fifties {c |}{col 25}{res}{space 2} .1263696{col 37}{space 2} .0227697{col 48}{space 1}    5.55{col 57}{space 3}0.000{col 65}{space 4} .0817394{col 78}{space 3} .1709997
{txt}{space 16}sixties {c |}{col 25}{res}{space 2} .1380553{col 37}{space 2} .0237308{col 48}{space 1}    5.82{col 57}{space 3}0.000{col 65}{space 4} .0915412{col 78}{space 3} .1845694
{txt}{space 14}seventies {c |}{col 25}{res}{space 2} .1319388{col 37}{space 2} .0168244{col 48}{space 1}    7.84{col 57}{space 3}0.000{col 65}{space 4} .0989619{col 78}{space 3} .1649157
{txt}{space 8}income2quartile {c |}{col 25}{res}{space 2}-.0154943{col 37}{space 2} .0140775{col 48}{space 1}   -1.10{col 57}{space 3}0.271{col 65}{space 4}-.0430871{col 78}{space 3} .0120985
{txt}{space 8}income3quartile {c |}{col 25}{res}{space 2}-.0147964{col 37}{space 2} .0164991{col 48}{space 1}   -0.90{col 57}{space 3}0.370{col 65}{space 4}-.0471357{col 78}{space 3} .0175429
{txt}{space 8}income4quartile {c |}{col 25}{res}{space 2} .0260167{col 37}{space 2} .0253647{col 48}{space 1}    1.03{col 57}{space 3}0.305{col 65}{space 4}-.0236998{col 78}{space 3} .0757333
{txt}{space 9}incomenoanswer {c |}{col 25}{res}{space 2} .0502153{col 37}{space 2} .0172375{col 48}{space 1}    2.91{col 57}{space 3}0.004{col 65}{space 4} .0164286{col 78}{space 3}  .084002
{txt}{space 17}female {c |}{col 25}{res}{space 2}-.0580748{col 37}{space 2} .0092163{col 48}{space 1}   -6.30{col 57}{space 3}0.000{col 65}{space 4}-.0761393{col 78}{space 3}-.0400102
{txt}{space 13}highschool {c |}{col 25}{res}{space 2} .0665456{col 37}{space 2} .0130988{col 48}{space 1}    5.08{col 57}{space 3}0.000{col 65}{space 4}  .040871{col 78}{space 3} .0922201
{txt}{space 16}college {c |}{col 25}{res}{space 2} .1427735{col 37}{space 2}  .011278{col 48}{space 1}   12.66{col 57}{space 3}0.000{col 65}{space 4} .1206678{col 78}{space 3} .1648792
{txt}{space 13}noreligion {c |}{col 25}{res}{space 2} .0737655{col 37}{space 2} .0230265{col 48}{space 1}    3.20{col 57}{space 3}0.001{col 65}{space 4} .0286318{col 78}{space 3} .1188991
{txt}{space 3}christiannotcatholic {c |}{col 25}{res}{space 2}-.0288053{col 37}{space 2} .0394753{col 48}{space 1}   -0.73{col 57}{space 3}0.466{col 65}{space 4}-.1061797{col 78}{space 3}  .048569
{txt}{space 15}catholic {c |}{col 25}{res}{space 2}-.0162899{col 37}{space 2}   .01957{col 48}{space 1}   -0.83{col 57}{space 3}0.405{col 65}{space 4}-.0546485{col 78}{space 3} .0220686
{txt}{space 9}fulltimeworker {c |}{col 25}{res}{space 2} .2358577{col 37}{space 2} .1758937{col 48}{space 1}    1.34{col 57}{space 3}0.180{col 65}{space 4}-.1089062{col 78}{space 3} .5806215
{txt}{space 9}parttimeworker {c |}{col 25}{res}{space 2} .3152578{col 37}{space 2}  .176485{col 48}{space 1}    1.79{col 57}{space 3}0.074{col 65}{space 4}-.0306651{col 78}{space 3} .6611808
{txt}{space 13}unemployed {c |}{col 25}{res}{space 2} .2773078{col 37}{space 2} .1957233{col 48}{space 1}    1.42{col 57}{space 3}0.157{col 65}{space 4}-.1063235{col 78}{space 3} .6609391
{txt}{space 11}selfemployed {c |}{col 25}{res}{space 2} .2459563{col 37}{space 2}  .216298{col 48}{space 1}    1.14{col 57}{space 3}0.256{col 65}{space 4}-.1780029{col 78}{space 3} .6699154
{txt}{space 16}outofLF {c |}{col 25}{res}{space 2} .2236907{col 37}{space 2} .1826746{col 48}{space 1}    1.22{col 57}{space 3}0.221{col 65}{space 4}-.1343642{col 78}{space 3} .5817455
{txt}{space 13}goodhealth {c |}{col 25}{res}{space 2} -.026796{col 37}{space 2} .0289971{col 48}{space 1}   -0.92{col 57}{space 3}0.355{col 65}{space 4}-.0836323{col 78}{space 3} .0300403
{txt}{space 18}white {c |}{col 25}{res}{space 2}-.2000724{col 37}{space 2} .1033457{col 48}{space 1}   -1.94{col 57}{space 3}0.053{col 65}{space 4}-.4026371{col 78}{space 3} .0024923
{txt}{space 18}black {c |}{col 25}{res}{space 2}-.0805475{col 37}{space 2} .0809298{col 48}{space 1}   -1.00{col 57}{space 3}0.320{col 65}{space 4}-.2391755{col 78}{space 3} .0780805
{txt}{space 17}latino {c |}{col 25}{res}{space 2}-.1005402{col 37}{space 2} .0912655{col 48}{space 1}   -1.10{col 57}{space 3}0.271{col 65}{space 4}-.2794269{col 78}{space 3} .0783465
{txt}{space 18}asian {c |}{col 25}{res}{space 2}-.1910452{col 37}{space 2} .0958762{col 48}{space 1}   -1.99{col 57}{space 3}0.046{col 65}{space 4}-.3789692{col 78}{space 3}-.0031211
{txt}{space 12}whitecollar {c |}{col 25}{res}{space 2} .0275556{col 37}{space 2} .0201277{col 48}{space 1}    1.37{col 57}{space 3}0.171{col 65}{space 4}-.0118961{col 78}{space 3} .0670074
{txt}{space 13}bluecollar {c |}{col 25}{res}{space 2}-.0421513{col 37}{space 2} .0230424{col 48}{space 1}   -1.83{col 57}{space 3}0.067{col 65}{space 4} -.087316{col 78}{space 3} .0030135
{txt}{space 10}serviceworker {c |}{col 25}{res}{space 2}-.0194945{col 37}{space 2} .0158493{col 48}{space 1}   -1.23{col 57}{space 3}0.219{col 65}{space 4}-.0505601{col 78}{space 3} .0115712
{txt}{space 12}mv_thirties {c |}{col 25}{res}{space 2}        0{col 37}{txt}  (omitted)
{space 12}mv_fourties {c |}{col 25}{res}{space 2}        0{col 37}{txt}  (omitted)
{space 13}mv_fifties {c |}{col 25}{res}{space 2}        0{col 37}{txt}  (omitted)
{space 13}mv_sixties {c |}{col 25}{res}{space 2}        0{col 37}{txt}  (omitted)
{space 11}mv_seventies {c |}{col 25}{res}{space 2}        0{col 37}{txt}  (omitted)
{space 5}mv_income2quartile {c |}{col 25}{res}{space 2}        0{col 37}{txt}  (omitted)
{space 5}mv_income3quartile {c |}{col 25}{res}{space 2}        0{col 37}{txt}  (omitted)
{space 5}mv_income4quartile {c |}{col 25}{res}{space 2}        0{col 37}{txt}  (omitted)
{space 6}mv_incomenoanswer {c |}{col 25}{res}{space 2} .0309726{col 37}{space 2} .0482044{col 48}{space 1}    0.64{col 57}{space 3}0.521{col 65}{space 4}-.0635114{col 78}{space 3} .1254565
{txt}{space 14}mv_female {c |}{col 25}{res}{space 2}        0{col 37}{txt}  (omitted)
{space 10}mv_highschool {c |}{col 25}{res}{space 2} .0804196{col 37}{space 2} .0297584{col 48}{space 1}    2.70{col 57}{space 3}0.007{col 65}{space 4} .0220911{col 78}{space 3} .1387481
{txt}{space 13}mv_college {c |}{col 25}{res}{space 2}        0{col 37}{txt}  (omitted)
{space 10}mv_noreligion {c |}{col 25}{res}{space 2} .0597061{col 37}{space 2} .0501014{col 48}{space 1}    1.19{col 57}{space 3}0.233{col 65}{space 4} -.038496{col 78}{space 3} .1579083
{txt}mv_christiannotcatholic {c |}{col 25}{res}{space 2}        0{col 37}{txt}  (omitted)
{space 12}mv_catholic {c |}{col 25}{res}{space 2}        0{col 37}{txt}  (omitted)
{space 6}mv_fulltimeworker {c |}{col 25}{res}{space 2}        0{col 37}{txt}  (omitted)
{space 6}mv_parttimeworker {c |}{col 25}{res}{space 2} .1744942{col 37}{space 2} .1654063{col 48}{space 1}    1.05{col 57}{space 3}0.291{col 65}{space 4}-.1497137{col 78}{space 3}  .498702
{txt}{space 10}mv_unemployed {c |}{col 25}{res}{space 2}        0{col 37}{txt}  (omitted)
{space 8}mv_selfemployed {c |}{col 25}{res}{space 2}-.2007937{col 37}{space 2} .1700924{col 48}{space 1}   -1.18{col 57}{space 3}0.238{col 65}{space 4}-.5341866{col 78}{space 3} .1325992
{txt}{space 13}mv_outofLF {c |}{col 25}{res}{space 2}        0{col 37}{txt}  (omitted)
{space 10}mv_goodhealth {c |}{col 25}{res}{space 2}-.1707718{col 37}{space 2} .1695894{col 48}{space 1}   -1.01{col 57}{space 3}0.314{col 65}{space 4}-.5031787{col 78}{space 3} .1616351
{txt}{space 15}mv_white {c |}{col 25}{res}{space 2}-.3459826{col 37}{space 2} .2250602{col 48}{space 1}   -1.54{col 57}{space 3}0.124{col 65}{space 4}-.7871162{col 78}{space 3} .0951509
{txt}{space 15}mv_black {c |}{col 25}{res}{space 2}        0{col 37}{txt}  (omitted)
{space 14}mv_latino {c |}{col 25}{res}{space 2}        0{col 37}{txt}  (omitted)
{space 15}mv_asian {c |}{col 25}{res}{space 2}        0{col 37}{txt}  (omitted)
{space 9}mv_whitecollar {c |}{col 25}{res}{space 2}-.2237859{col 37}{space 2} .1744915{col 48}{space 1}   -1.28{col 57}{space 3}0.200{col 65}{space 4}-.5658014{col 78}{space 3} .1182296
{txt}{space 10}mv_bluecollar {c |}{col 25}{res}{space 2}        0{col 37}{txt}  (omitted)
{space 7}mv_serviceworker {c |}{col 25}{res}{space 2}        0{col 37}{txt}  (omitted)
{space 23} {c |}
{space 16}country {c |}
{space 15}Austria  {c |}{col 25}{res}{space 2}-.3348602{col 37}{space 2}  .299651{col 48}{space 1}   -1.12{col 57}{space 3}0.264{col 65}{space 4} -.922197{col 78}{space 3} .2524767
{txt}{space 16}Brazil  {c |}{col 25}{res}{space 2}-.1406598{col 37}{space 2} .0383538{col 48}{space 1}   -3.67{col 57}{space 3}0.000{col 65}{space 4} -.215836{col 78}{space 3}-.0654836
{txt}{space 16}France  {c |}{col 25}{res}{space 2}        0{col 37}{txt}  (omitted)
{space 15}Germany  {c |}{col 25}{res}{space 2}-.4120205{col 37}{space 2}  .320501{col 48}{space 1}   -1.29{col 57}{space 3}0.199{col 65}{space 4}-1.040225{col 78}{space 3} .2161837
{txt}{space 17}Italy  {c |}{col 25}{res}{space 2}-.5017715{col 37}{space 2} .2904421{col 48}{space 1}   -1.73{col 57}{space 3}0.084{col 65}{space 4}-1.071058{col 78}{space 3} .0675151
{txt}{space 11}New_Zealand  {c |}{col 25}{res}{space 2}        0{col 37}{txt}  (omitted)
{space 16}Poland  {c |}{col 25}{res}{space 2} .0130847{col 37}{space 2} .0422247{col 48}{space 1}    0.31{col 57}{space 3}0.757{col 65}{space 4}-.0696787{col 78}{space 3} .0958481
{txt}{space 17}Spain  {c |}{col 25}{res}{space 2}        0{col 37}{txt}  (omitted)
{space 16}Sweden  {c |}{col 25}{res}{space 2}-.1478817{col 37}{space 2} .0496351{col 48}{space 1}   -2.98{col 57}{space 3}0.003{col 65}{space 4}  -.24517{col 78}{space 3}-.0505935
{txt}{space 20}UK  {c |}{col 25}{res}{space 2}        0{col 37}{txt}  (omitted)
{space 20}US  {c |}{col 25}{res}{space 2}        0{col 37}{txt}  (omitted)
{space 23} {c |}
{space 18}_cons {c |}{col 25}{res}{space 2} .5963831{col 37}{space 2}  .084133{col 48}{space 1}    7.09{col 57}{space 3}0.000{col 65}{space 4} .4314765{col 78}{space 3} .7612896
{txt}{hline 24}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{help ivreg2##idtest:Underidentification test} (Kleibergen-Paap rk LM statistic):{res}{col 71}  10.052
{txt}{col 52}Chi-sq({res}1{txt}) P-val =  {res}{col 73}0.0015
{txt}{hline 78}
{help ivreg2##widtest:Weak identification test} (Cragg-Donald Wald F statistic):{res}{col 71}  10.017
{txt}                         (Kleibergen-Paap rk Wald F statistic):{res}{col 71}  10.047
{txt}Stock-Yogo weak ID test critical values:{res}{txt}{col 42}10% maximal IV size{res}{col 73} 16.38
{txt}{col 42}15% maximal IV size{res}{col 73}  8.96
{txt}{col 42}20% maximal IV size{res}{col 73}  6.66
{txt}{col 42}25% maximal IV size{res}{col 73}  5.53
{txt}Source: Stock-Yogo (2005).  Reproduced by permission.
NB: Critical values are for Cragg-Donald F statistic and i.i.d. errors.
{hline 78}
{help ivreg2##overidtests:Hansen J statistic} (overidentification test of all instruments):{res}{col 71}   0.000
{txt}{col 50}(equation exactly identified)
{hline 78}
Instrumented:{col 23}satis_head
Included instruments:{col 23}thirties fourties fifties sixties seventies
{col 23}income2quartile income3quartile income4quartile
{col 23}incomenoanswer female highschool college noreligion
{col 23}christiannotcatholic catholic fulltimeworker
{col 23}parttimeworker unemployed selfemployed outofLF goodhealth
{col 23}white black latino asian whitecollar bluecollar
{col 23}serviceworker mv_incomenoanswer mv_highschool
{col 23}mv_noreligion mv_parttimeworker mv_selfemployed
{col 23}mv_goodhealth mv_white mv_whitecollar 2.country 3.country
{col 23}5.country 6.country 8.country 10.country
Excluded instruments:{col 23}treatc
Dropped collinear:{col 23}mv_thirties mv_fourties mv_fifties mv_sixties
{col 23}mv_seventies mv_income2quartile mv_income3quartile
{col 23}mv_income4quartile mv_female mv_college
{col 23}mv_christiannotcatholic mv_catholic mv_fulltimeworker
{col 23}mv_unemployed mv_outofLF mv_black mv_latino mv_asian
{col 23}mv_bluecollar mv_serviceworker 4.country 7.country
{col 23}9.country 11.country 12.country
{hline 78}
({res}est4{txt} stored)

{com}. 
. estadd scalar fstat = e(widstat)

{txt}added scalar:
              e(fstat) =  {res}10.047106
{txt}
{com}. estadd local IV "SumIV", replace

{txt}added macro:
                 e(IV) : "{res:SumIV}"

{com}. estadd local Controls "X", replace

{txt}added macro:
           e(Controls) : "{res:X}"

{com}. estadd local CFE "X", replace

{txt}added macro:
                e(CFE) : "{res:X}"

{com}. get_mean

{txt}added scalar:
                 e(av) =  {res}.588
{txt}
{com}. 
. /* E.8. Col 5: Standardized Index with 16 instruments */
. eststo: ivreg2 index_efficacy ///
>         (satis_head = TH1_TE1 TH1_TE2 TH1_TE3 TH1_TE4 TH2_TE1 TH2_TE2 TH2_TE3 TH2_TE4 TH3_TE1 TH3_TE2 TH3_TE3 TH3_TE4 TH4_TE1 TH4_TE2 TH4_TE3) $controls $mv_controls  i.country, small robust 
{txt}Warning - collinearities detected
Vars dropped:{col 21}mv_thirties mv_fourties mv_fifties mv_sixties mv_seventies
{col 21}mv_income2quartile mv_income3quartile mv_income4quartile
{col 21}mv_female mv_college mv_christiannotcatholic mv_catholic
{col 21}mv_fulltimeworker mv_unemployed mv_outofLF mv_black
{col 21}mv_latino mv_asian mv_bluecollar mv_serviceworker 4.country
{col 21}7.country 9.country 11.country 12.country
{res}
{txt}IV (2SLS) estimation
{hline 20}

Estimates efficient for homoskedasticity only
Statistics robust to heteroskedasticity

{col 55}Number of obs = {res}   22537
{txt}{col 55}F( 43, 22493) = {res}   36.28
{txt}{col 55}Prob > F      = {res}  0.0000
{txt}Total (centered) SS     = {res} 13161.85715{txt}{col 55}Centered R2   = {res}  0.0664
{txt}Total (uncentered) SS   = {res} 13161.85716{txt}{col 55}Uncentered R2 = {res}  0.0664
{txt}Residual SS             = {res} 12288.35051{txt}{col 55}Root MSE      = {res}   .7391

{txt}{hline 24}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 25}{c |}{col 37}    Robust
{col 1}         index_efficacy{col 25}{c |} Coefficient{col 37}  std. err.{col 49}      t{col 57}   P>|t|{col 65}     [95% con{col 78}f. interval]
{hline 24}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 13}satis_head {c |}{col 25}{res}{space 2} .9087845{col 37}{space 2}  .524402{col 48}{space 1}    1.73{col 57}{space 3}0.083{col 65}{space 4}-.1190799{col 78}{space 3} 1.936649
{txt}{space 15}thirties {c |}{col 25}{res}{space 2}-.0297344{col 37}{space 2}  .019162{col 48}{space 1}   -1.55{col 57}{space 3}0.121{col 65}{space 4}-.0672932{col 78}{space 3} .0078243
{txt}{space 15}fourties {c |}{col 25}{res}{space 2} .0458842{col 37}{space 2} .0243104{col 48}{space 1}    1.89{col 57}{space 3}0.059{col 65}{space 4}-.0017658{col 78}{space 3} .0935342
{txt}{space 16}fifties {c |}{col 25}{res}{space 2}  .085593{col 37}{space 2} .0253457{col 48}{space 1}    3.38{col 57}{space 3}0.001{col 65}{space 4} .0359137{col 78}{space 3} .1352723
{txt}{space 16}sixties {c |}{col 25}{res}{space 2} .0926451{col 37}{space 2}  .026012{col 48}{space 1}    3.56{col 57}{space 3}0.000{col 65}{space 4} .0416597{col 78}{space 3} .1436306
{txt}{space 14}seventies {c |}{col 25}{res}{space 2} .1240036{col 37}{space 2}  .022845{col 48}{space 1}    5.43{col 57}{space 3}0.000{col 65}{space 4} .0792258{col 78}{space 3} .1687815
{txt}{space 8}income2quartile {c |}{col 25}{res}{space 2} .0171456{col 37}{space 2} .0172376{col 48}{space 1}    0.99{col 57}{space 3}0.320{col 65}{space 4}-.0166413{col 78}{space 3} .0509325
{txt}{space 8}income3quartile {c |}{col 25}{res}{space 2} .0271046{col 37}{space 2} .0189986{col 48}{space 1}    1.43{col 57}{space 3}0.154{col 65}{space 4} -.010134{col 78}{space 3} .0643432
{txt}{space 8}income4quartile {c |}{col 25}{res}{space 2} .1243809{col 37}{space 2} .0265343{col 48}{space 1}    4.69{col 57}{space 3}0.000{col 65}{space 4} .0723718{col 78}{space 3} .1763899
{txt}{space 9}incomenoanswer {c |}{col 25}{res}{space 2} .0466571{col 37}{space 2} .0237534{col 48}{space 1}    1.96{col 57}{space 3}0.050{col 65}{space 4} .0000988{col 78}{space 3} .0932153
{txt}{space 17}female {c |}{col 25}{res}{space 2} -.071388{col 37}{space 2} .0115462{col 48}{space 1}   -6.18{col 57}{space 3}0.000{col 65}{space 4}-.0940194{col 78}{space 3}-.0487566
{txt}{space 13}highschool {c |}{col 25}{res}{space 2} .1084902{col 37}{space 2} .0171897{col 48}{space 1}    6.31{col 57}{space 3}0.000{col 65}{space 4} .0747972{col 78}{space 3} .1421831
{txt}{space 16}college {c |}{col 25}{res}{space 2} .2698768{col 37}{space 2} .0155487{col 48}{space 1}   17.36{col 57}{space 3}0.000{col 65}{space 4} .2394002{col 78}{space 3} .3003535
{txt}{space 13}noreligion {c |}{col 25}{res}{space 2} .0785851{col 37}{space 2} .0277707{col 48}{space 1}    2.83{col 57}{space 3}0.005{col 65}{space 4} .0241527{col 78}{space 3} .1330175
{txt}{space 3}christiannotcatholic {c |}{col 25}{res}{space 2} .0138422{col 37}{space 2} .0419515{col 48}{space 1}    0.33{col 57}{space 3}0.741{col 65}{space 4}-.0683857{col 78}{space 3} .0960701
{txt}{space 15}catholic {c |}{col 25}{res}{space 2}-.0112199{col 37}{space 2} .0256853{col 48}{space 1}   -0.44{col 57}{space 3}0.662{col 65}{space 4}-.0615648{col 78}{space 3}  .039125
{txt}{space 9}fulltimeworker {c |}{col 25}{res}{space 2} .0050969{col 37}{space 2} .1668011{col 48}{space 1}    0.03{col 57}{space 3}0.976{col 65}{space 4}-.3218449{col 78}{space 3} .3320387
{txt}{space 9}parttimeworker {c |}{col 25}{res}{space 2} .1030538{col 37}{space 2}  .171598{col 48}{space 1}    0.60{col 57}{space 3}0.548{col 65}{space 4}-.2332901{col 78}{space 3} .4393978
{txt}{space 13}unemployed {c |}{col 25}{res}{space 2} .0571935{col 37}{space 2} .1865273{col 48}{space 1}    0.31{col 57}{space 3}0.759{col 65}{space 4} -.308413{col 78}{space 3} .4227999
{txt}{space 11}selfemployed {c |}{col 25}{res}{space 2}-.0009368{col 37}{space 2} .2155119{col 48}{space 1}   -0.00{col 57}{space 3}0.997{col 65}{space 4}-.4233551{col 78}{space 3} .4214816
{txt}{space 16}outofLF {c |}{col 25}{res}{space 2}  .021494{col 37}{space 2} .1735588{col 48}{space 1}    0.12{col 57}{space 3}0.901{col 65}{space 4}-.3186933{col 78}{space 3} .3616813
{txt}{space 13}goodhealth {c |}{col 25}{res}{space 2} .0108177{col 37}{space 2} .0283631{col 48}{space 1}    0.38{col 57}{space 3}0.703{col 65}{space 4} -.044776{col 78}{space 3} .0664113
{txt}{space 18}white {c |}{col 25}{res}{space 2}-.2556968{col 37}{space 2} .1244809{col 48}{space 1}   -2.05{col 57}{space 3}0.040{col 65}{space 4}-.4996881{col 78}{space 3}-.0117056
{txt}{space 18}black {c |}{col 25}{res}{space 2}-.0676133{col 37}{space 2} .1181621{col 48}{space 1}   -0.57{col 57}{space 3}0.567{col 65}{space 4}-.2992193{col 78}{space 3} .1639927
{txt}{space 17}latino {c |}{col 25}{res}{space 2}-.0360171{col 37}{space 2} .1346801{col 48}{space 1}   -0.27{col 57}{space 3}0.789{col 65}{space 4}-.2999994{col 78}{space 3} .2279652
{txt}{space 18}asian {c |}{col 25}{res}{space 2}-.2179942{col 37}{space 2} .1265525{col 48}{space 1}   -1.72{col 57}{space 3}0.085{col 65}{space 4}-.4660459{col 78}{space 3} .0300575
{txt}{space 12}whitecollar {c |}{col 25}{res}{space 2} .0477524{col 37}{space 2} .0284976{col 48}{space 1}    1.68{col 57}{space 3}0.094{col 65}{space 4}-.0081049{col 78}{space 3} .1036098
{txt}{space 13}bluecollar {c |}{col 25}{res}{space 2}-.1420204{col 37}{space 2} .0286797{col 48}{space 1}   -4.95{col 57}{space 3}0.000{col 65}{space 4}-.1982346{col 78}{space 3}-.0858063
{txt}{space 10}serviceworker {c |}{col 25}{res}{space 2}-.0412927{col 37}{space 2} .0221305{col 48}{space 1}   -1.87{col 57}{space 3}0.062{col 65}{space 4}-.0846699{col 78}{space 3} .0020846
{txt}{space 12}mv_thirties {c |}{col 25}{res}{space 2}        0{col 37}{txt}  (omitted)
{space 12}mv_fourties {c |}{col 25}{res}{space 2}        0{col 37}{txt}  (omitted)
{space 13}mv_fifties {c |}{col 25}{res}{space 2}        0{col 37}{txt}  (omitted)
{space 13}mv_sixties {c |}{col 25}{res}{space 2}        0{col 37}{txt}  (omitted)
{space 11}mv_seventies {c |}{col 25}{res}{space 2}        0{col 37}{txt}  (omitted)
{space 5}mv_income2quartile {c |}{col 25}{res}{space 2}        0{col 37}{txt}  (omitted)
{space 5}mv_income3quartile {c |}{col 25}{res}{space 2}        0{col 37}{txt}  (omitted)
{space 5}mv_income4quartile {c |}{col 25}{res}{space 2}        0{col 37}{txt}  (omitted)
{space 6}mv_incomenoanswer {c |}{col 25}{res}{space 2}-.1126505{col 37}{space 2} .0526389{col 48}{space 1}   -2.14{col 57}{space 3}0.032{col 65}{space 4}-.2158263{col 78}{space 3}-.0094746
{txt}{space 14}mv_female {c |}{col 25}{res}{space 2}        0{col 37}{txt}  (omitted)
{space 10}mv_highschool {c |}{col 25}{res}{space 2} .1686028{col 37}{space 2} .0414976{col 48}{space 1}    4.06{col 57}{space 3}0.000{col 65}{space 4} .0872646{col 78}{space 3} .2499409
{txt}{space 13}mv_college {c |}{col 25}{res}{space 2}        0{col 37}{txt}  (omitted)
{space 10}mv_noreligion {c |}{col 25}{res}{space 2} .1078578{col 37}{space 2} .0748476{col 48}{space 1}    1.44{col 57}{space 3}0.150{col 65}{space 4}-.0388488{col 78}{space 3} .2545644
{txt}mv_christiannotcatholic {c |}{col 25}{res}{space 2}        0{col 37}{txt}  (omitted)
{space 12}mv_catholic {c |}{col 25}{res}{space 2}        0{col 37}{txt}  (omitted)
{space 6}mv_fulltimeworker {c |}{col 25}{res}{space 2}        0{col 37}{txt}  (omitted)
{space 6}mv_parttimeworker {c |}{col 25}{res}{space 2}-.0705819{col 37}{space 2}  .160461{col 48}{space 1}   -0.44{col 57}{space 3}0.660{col 65}{space 4}-.3850966{col 78}{space 3} .2439329
{txt}{space 10}mv_unemployed {c |}{col 25}{res}{space 2}        0{col 37}{txt}  (omitted)
{space 8}mv_selfemployed {c |}{col 25}{res}{space 2} .0080019{col 37}{space 2}  .165322{col 48}{space 1}    0.05{col 57}{space 3}0.961{col 65}{space 4}-.3160406{col 78}{space 3} .3320445
{txt}{space 13}mv_outofLF {c |}{col 25}{res}{space 2}        0{col 37}{txt}  (omitted)
{space 10}mv_goodhealth {c |}{col 25}{res}{space 2} -.164263{col 37}{space 2} .1730346{col 48}{space 1}   -0.95{col 57}{space 3}0.342{col 65}{space 4}-.5034229{col 78}{space 3} .1748969
{txt}{space 15}mv_white {c |}{col 25}{res}{space 2}-.3215816{col 37}{space 2} .2245686{col 48}{space 1}   -1.43{col 57}{space 3}0.152{col 65}{space 4}-.7617516{col 78}{space 3} .1185884
{txt}{space 15}mv_black {c |}{col 25}{res}{space 2}        0{col 37}{txt}  (omitted)
{space 14}mv_latino {c |}{col 25}{res}{space 2}        0{col 37}{txt}  (omitted)
{space 15}mv_asian {c |}{col 25}{res}{space 2}        0{col 37}{txt}  (omitted)
{space 9}mv_whitecollar {c |}{col 25}{res}{space 2}-.1228244{col 37}{space 2} .1689053{col 48}{space 1}   -0.73{col 57}{space 3}0.467{col 65}{space 4}-.4538906{col 78}{space 3} .2082418
{txt}{space 10}mv_bluecollar {c |}{col 25}{res}{space 2}        0{col 37}{txt}  (omitted)
{space 7}mv_serviceworker {c |}{col 25}{res}{space 2}        0{col 37}{txt}  (omitted)
{space 23} {c |}
{space 16}country {c |}
{space 15}Austria  {c |}{col 25}{res}{space 2}-.2119449{col 37}{space 2} .2934514{col 48}{space 1}   -0.72{col 57}{space 3}0.470{col 65}{space 4}-.7871299{col 78}{space 3} .3632402
{txt}{space 16}Brazil  {c |}{col 25}{res}{space 2}-.3647395{col 37}{space 2} .0551703{col 48}{space 1}   -6.61{col 57}{space 3}0.000{col 65}{space 4}-.4728771{col 78}{space 3}-.2566019
{txt}{space 16}France  {c |}{col 25}{res}{space 2}        0{col 37}{txt}  (omitted)
{space 15}Germany  {c |}{col 25}{res}{space 2}-.3793537{col 37}{space 2}   .31216{col 48}{space 1}   -1.22{col 57}{space 3}0.224{col 65}{space 4} -.991209{col 78}{space 3} .2325016
{txt}{space 17}Italy  {c |}{col 25}{res}{space 2}-.6291603{col 37}{space 2} .2897692{col 48}{space 1}   -2.17{col 57}{space 3}0.030{col 65}{space 4}-1.197128{col 78}{space 3}-.0611926
{txt}{space 11}New_Zealand  {c |}{col 25}{res}{space 2}        0{col 37}{txt}  (omitted)
{space 16}Poland  {c |}{col 25}{res}{space 2}-.0922679{col 37}{space 2} .0556992{col 48}{space 1}   -1.66{col 57}{space 3}0.098{col 65}{space 4}-.2014423{col 78}{space 3} .0169065
{txt}{space 17}Spain  {c |}{col 25}{res}{space 2}        0{col 37}{txt}  (omitted)
{space 16}Sweden  {c |}{col 25}{res}{space 2}-.0977635{col 37}{space 2} .0610912{col 48}{space 1}   -1.60{col 57}{space 3}0.110{col 65}{space 4}-.2175065{col 78}{space 3} .0219796
{txt}{space 20}UK  {c |}{col 25}{res}{space 2}        0{col 37}{txt}  (omitted)
{space 20}US  {c |}{col 25}{res}{space 2}        0{col 37}{txt}  (omitted)
{space 23} {c |}
{space 18}_cons {c |}{col 25}{res}{space 2}-.1814822{col 37}{space 2} .1190562{col 48}{space 1}   -1.52{col 57}{space 3}0.127{col 65}{space 4}-.4148407{col 78}{space 3} .0518762
{txt}{hline 24}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{help ivreg2##idtest:Underidentification test} (Kleibergen-Paap rk LM statistic):{res}{col 71}  23.338
{txt}{col 52}Chi-sq({res}15{txt}) P-val =  {res}{col 73}0.0772
{txt}{hline 78}
{help ivreg2##widtest:Weak identification test} (Cragg-Donald Wald F statistic):{res}{col 71}   1.528
{txt}                         (Kleibergen-Paap rk Wald F statistic):{res}{col 71}   1.558
{txt}Stock-Yogo weak ID test critical values:{res}{txt}{col 42} 5% maximal IV relative bias{res}{col 73} 21.23
{txt}{col 42}10% maximal IV relative bias{res}{col 73} 11.51
{txt}{col 42}20% maximal IV relative bias{res}{col 73}  6.42
{txt}{col 42}30% maximal IV relative bias{res}{col 73}  4.63
{txt}{col 42}10% maximal IV size{res}{col 73} 50.39
{txt}{col 42}15% maximal IV size{res}{col 73} 26.80
{txt}{col 42}20% maximal IV size{res}{col 73} 18.72
{txt}{col 42}25% maximal IV size{res}{col 73} 14.60
{txt}Source: Stock-Yogo (2005).  Reproduced by permission.
NB: Critical values are for Cragg-Donald F statistic and i.i.d. errors.
{hline 78}
{help ivreg2##overidtests:Hansen J statistic} (overidentification test of all instruments):{res}{col 71}  20.176
{txt}{col 52}Chi-sq({res}14{txt}) P-val =  {res}{col 73}0.1247
{txt}{hline 78}
Instrumented:{col 23}satis_head
Included instruments:{col 23}thirties fourties fifties sixties seventies
{col 23}income2quartile income3quartile income4quartile
{col 23}incomenoanswer female highschool college noreligion
{col 23}christiannotcatholic catholic fulltimeworker
{col 23}parttimeworker unemployed selfemployed outofLF goodhealth
{col 23}white black latino asian whitecollar bluecollar
{col 23}serviceworker mv_incomenoanswer mv_highschool
{col 23}mv_noreligion mv_parttimeworker mv_selfemployed
{col 23}mv_goodhealth mv_white mv_whitecollar 2.country 3.country
{col 23}5.country 6.country 8.country 10.country
Excluded instruments:{col 23}TH1_TE1 TH1_TE2 TH1_TE3 TH1_TE4 TH2_TE1 TH2_TE2 TH2_TE3
{col 23}TH2_TE4 TH3_TE1 TH3_TE2 TH3_TE3 TH3_TE4 TH4_TE1 TH4_TE2
{col 23}TH4_TE3
Dropped collinear:{col 23}mv_thirties mv_fourties mv_fifties mv_sixties
{col 23}mv_seventies mv_income2quartile mv_income3quartile
{col 23}mv_income4quartile mv_female mv_college
{col 23}mv_christiannotcatholic mv_catholic mv_fulltimeworker
{col 23}mv_unemployed mv_outofLF mv_black mv_latino mv_asian
{col 23}mv_bluecollar mv_serviceworker 4.country 7.country
{col 23}9.country 11.country 12.country
{hline 78}
({res}est5{txt} stored)

{com}. 
. estadd scalar fstat = e(widstat)

{txt}added scalar:
              e(fstat) =  {res}1.5583507
{txt}
{com}. estadd local IV "16 IVs", replace

{txt}added macro:
                 e(IV) : "{res:16 IVs}"

{com}. estadd local Controls "X", replace

{txt}added macro:
           e(Controls) : "{res:X}"

{com}. estadd local CFE "X", replace

{txt}added macro:
                e(CFE) : "{res:X}"

{com}. get_mean

{txt}added scalar:
                 e(av) =  {res}0
{txt}
{com}. 
. /* E.8. Col 6: Standardized Index with 1 instrument */
. eststo: ivreg2 index_efficacy ///
>         (satis_head = treatc ) $controls $mv_controls  i.country, small robust 
{txt}Warning - collinearities detected
Vars dropped:{col 21}mv_thirties mv_fourties mv_fifties mv_sixties mv_seventies
{col 21}mv_income2quartile mv_income3quartile mv_income4quartile
{col 21}mv_female mv_college mv_christiannotcatholic mv_catholic
{col 21}mv_fulltimeworker mv_unemployed mv_outofLF mv_black
{col 21}mv_latino mv_asian mv_bluecollar mv_serviceworker 4.country
{col 21}7.country 9.country 11.country 12.country
{res}
{txt}IV (2SLS) estimation
{hline 20}

Estimates efficient for homoskedasticity only
Statistics robust to heteroskedasticity

{col 55}Number of obs = {res}   22537
{txt}{col 55}F( 43, 22493) = {res}   34.35
{txt}{col 55}Prob > F      = {res}  0.0000
{txt}Total (centered) SS     = {res} 13161.85715{txt}{col 55}Centered R2   = {res}  0.0097
{txt}Total (uncentered) SS   = {res} 13161.85716{txt}{col 55}Uncentered R2 = {res}  0.0097
{txt}Residual SS             = {res} 13033.90136{txt}{col 55}Root MSE      = {res}   .7612

{txt}{hline 24}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 25}{c |}{col 37}    Robust
{col 1}         index_efficacy{col 25}{c |} Coefficient{col 37}  std. err.{col 49}      t{col 57}   P>|t|{col 65}     [95% con{col 78}f. interval]
{hline 24}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 13}satis_head {c |}{col 25}{res}{space 2} 1.223439{col 37}{space 2} .8156003{col 48}{space 1}    1.50{col 57}{space 3}0.134{col 65}{space 4}-.3751941{col 78}{space 3} 2.822072
{txt}{space 15}thirties {c |}{col 25}{res}{space 2}-.0255598{col 37}{space 2}  .021437{col 48}{space 1}   -1.19{col 57}{space 3}0.233{col 65}{space 4}-.0675779{col 78}{space 3} .0164582
{txt}{space 15}fourties {c |}{col 25}{res}{space 2} .0558292{col 37}{space 2} .0315678{col 48}{space 1}    1.77{col 57}{space 3}0.077{col 65}{space 4}-.0060459{col 78}{space 3} .1177044
{txt}{space 16}fifties {c |}{col 25}{res}{space 2}  .096231{col 37}{space 2} .0333588{col 48}{space 1}    2.88{col 57}{space 3}0.004{col 65}{space 4} .0308455{col 78}{space 3} .1616165
{txt}{space 16}sixties {c |}{col 25}{res}{space 2} .1038806{col 37}{space 2} .0346909{col 48}{space 1}    2.99{col 57}{space 3}0.003{col 65}{space 4} .0358841{col 78}{space 3} .1718771
{txt}{space 14}seventies {c |}{col 25}{res}{space 2} .1282396{col 37}{space 2}  .024951{col 48}{space 1}    5.14{col 57}{space 3}0.000{col 65}{space 4}  .079334{col 78}{space 3} .1771452
{txt}{space 8}income2quartile {c |}{col 25}{res}{space 2} .0117756{col 37}{space 2} .0206188{col 48}{space 1}    0.57{col 57}{space 3}0.568{col 65}{space 4}-.0286386{col 78}{space 3} .0521898
{txt}{space 8}income3quartile {c |}{col 25}{res}{space 2} .0199889{col 37}{space 2} .0239826{col 48}{space 1}    0.83{col 57}{space 3}0.405{col 65}{space 4}-.0270186{col 78}{space 3} .0669965
{txt}{space 8}income4quartile {c |}{col 25}{res}{space 2}  .111449{col 37}{space 2} .0371254{col 48}{space 1}    3.00{col 57}{space 3}0.003{col 65}{space 4} .0386806{col 78}{space 3} .1842175
{txt}{space 9}incomenoanswer {c |}{col 25}{res}{space 2} .0498814{col 37}{space 2} .0251802{col 48}{space 1}    1.98{col 57}{space 3}0.048{col 65}{space 4} .0005265{col 78}{space 3} .0992363
{txt}{space 17}female {c |}{col 25}{res}{space 2} -.074737{col 37}{space 2} .0135249{col 48}{space 1}   -5.53{col 57}{space 3}0.000{col 65}{space 4}-.1012468{col 78}{space 3}-.0482271
{txt}{space 13}highschool {c |}{col 25}{res}{space 2} .1055422{col 37}{space 2} .0185959{col 48}{space 1}    5.68{col 57}{space 3}0.000{col 65}{space 4} .0690929{col 78}{space 3} .1419914
{txt}{space 16}college {c |}{col 25}{res}{space 2} .2690489{col 37}{space 2}   .01596{col 48}{space 1}   16.86{col 57}{space 3}0.000{col 65}{space 4} .2377662{col 78}{space 3} .3003317
{txt}{space 13}noreligion {c |}{col 25}{res}{space 2}  .088356{col 37}{space 2} .0343343{col 48}{space 1}    2.57{col 57}{space 3}0.010{col 65}{space 4} .0210584{col 78}{space 3} .1556536
{txt}{space 3}christiannotcatholic {c |}{col 25}{res}{space 2}-.0062194{col 37}{space 2} .0583557{col 48}{space 1}   -0.11{col 57}{space 3}0.915{col 65}{space 4}-.1206006{col 78}{space 3} .1081618
{txt}{space 15}catholic {c |}{col 25}{res}{space 2}-.0179583{col 37}{space 2} .0296055{col 48}{space 1}   -0.61{col 57}{space 3}0.544{col 65}{space 4}-.0759872{col 78}{space 3} .0400706
{txt}{space 9}fulltimeworker {c |}{col 25}{res}{space 2} .1036543{col 37}{space 2} .2571848{col 48}{space 1}    0.40{col 57}{space 3}0.687{col 65}{space 4}-.4004457{col 78}{space 3} .6077543
{txt}{space 9}parttimeworker {c |}{col 25}{res}{space 2} .1995815{col 37}{space 2} .2576751{col 48}{space 1}    0.77{col 57}{space 3}0.439{col 65}{space 4}-.3054795{col 78}{space 3} .7046425
{txt}{space 13}unemployed {c |}{col 25}{res}{space 2} .1663333{col 37}{space 2}  .286017{col 48}{space 1}    0.58{col 57}{space 3}0.561{col 65}{space 4}  -.39428{col 78}{space 3} .7269465
{txt}{space 11}selfemployed {c |}{col 25}{res}{space 2} .1155167{col 37}{space 2}  .317414{col 48}{space 1}    0.36{col 57}{space 3}0.716{col 65}{space 4}-.5066369{col 78}{space 3} .7376702
{txt}{space 16}outofLF {c |}{col 25}{res}{space 2} .1238491{col 37}{space 2} .2672328{col 48}{space 1}    0.46{col 57}{space 3}0.643{col 65}{space 4}-.3999457{col 78}{space 3}  .647644
{txt}{space 13}goodhealth {c |}{col 25}{res}{space 2}-.0049568{col 37}{space 2} .0423785{col 48}{space 1}   -0.12{col 57}{space 3}0.907{col 65}{space 4}-.0880216{col 78}{space 3}  .078108
{txt}{space 18}white {c |}{col 25}{res}{space 2}-.2971455{col 37}{space 2} .1550295{col 48}{space 1}   -1.92{col 57}{space 3}0.055{col 65}{space 4}-.6010141{col 78}{space 3} .0067232
{txt}{space 18}black {c |}{col 25}{res}{space 2}-.0710392{col 37}{space 2} .1262959{col 48}{space 1}   -0.56{col 57}{space 3}0.574{col 65}{space 4}-.3185878{col 78}{space 3} .1765095
{txt}{space 17}latino {c |}{col 25}{res}{space 2}-.0471171{col 37}{space 2} .1452698{col 48}{space 1}   -0.32{col 57}{space 3}0.746{col 65}{space 4}-.3318559{col 78}{space 3} .2376217
{txt}{space 18}asian {c |}{col 25}{res}{space 2}-.2408575{col 37}{space 2} .1425202{col 48}{space 1}   -1.69{col 57}{space 3}0.091{col 65}{space 4}-.5202069{col 78}{space 3} .0384919
{txt}{space 12}whitecollar {c |}{col 25}{res}{space 2} .0532185{col 37}{space 2} .0312357{col 48}{space 1}    1.70{col 57}{space 3}0.088{col 65}{space 4}-.0080056{col 78}{space 3} .1144427
{txt}{space 13}bluecollar {c |}{col 25}{res}{space 2}-.1346286{col 37}{space 2} .0331081{col 48}{space 1}   -4.07{col 57}{space 3}0.000{col 65}{space 4}-.1995227{col 78}{space 3}-.0697345
{txt}{space 10}serviceworker {c |}{col 25}{res}{space 2}-.0382259{col 37}{space 2} .0235636{col 48}{space 1}   -1.62{col 57}{space 3}0.105{col 65}{space 4}-.0844122{col 78}{space 3} .0079605
{txt}{space 12}mv_thirties {c |}{col 25}{res}{space 2}        0{col 37}{txt}  (omitted)
{space 12}mv_fourties {c |}{col 25}{res}{space 2}        0{col 37}{txt}  (omitted)
{space 13}mv_fifties {c |}{col 25}{res}{space 2}        0{col 37}{txt}  (omitted)
{space 13}mv_sixties {c |}{col 25}{res}{space 2}        0{col 37}{txt}  (omitted)
{space 11}mv_seventies {c |}{col 25}{res}{space 2}        0{col 37}{txt}  (omitted)
{space 5}mv_income2quartile {c |}{col 25}{res}{space 2}        0{col 37}{txt}  (omitted)
{space 5}mv_income3quartile {c |}{col 25}{res}{space 2}        0{col 37}{txt}  (omitted)
{space 5}mv_income4quartile {c |}{col 25}{res}{space 2}        0{col 37}{txt}  (omitted)
{space 6}mv_incomenoanswer {c |}{col 25}{res}{space 2}-.0890763{col 37}{space 2} .0708292{col 48}{space 1}   -1.26{col 57}{space 3}0.209{col 65}{space 4}-.2279065{col 78}{space 3}  .049754
{txt}{space 14}mv_female {c |}{col 25}{res}{space 2}        0{col 37}{txt}  (omitted)
{space 10}mv_highschool {c |}{col 25}{res}{space 2} .1719944{col 37}{space 2} .0426896{col 48}{space 1}    4.03{col 57}{space 3}0.000{col 65}{space 4} .0883197{col 78}{space 3}  .255669
{txt}{space 13}mv_college {c |}{col 25}{res}{space 2}        0{col 37}{txt}  (omitted)
{space 10}mv_noreligion {c |}{col 25}{res}{space 2} .1063866{col 37}{space 2} .0767149{col 48}{space 1}    1.39{col 57}{space 3}0.166{col 65}{space 4}-.0439799{col 78}{space 3} .2567532
{txt}mv_christiannotcatholic {c |}{col 25}{res}{space 2}        0{col 37}{txt}  (omitted)
{space 12}mv_catholic {c |}{col 25}{res}{space 2}        0{col 37}{txt}  (omitted)
{space 6}mv_fulltimeworker {c |}{col 25}{res}{space 2}        0{col 37}{txt}  (omitted)
{space 6}mv_parttimeworker {c |}{col 25}{res}{space 2} .0205933{col 37}{space 2} .2420374{col 48}{space 1}    0.09{col 57}{space 3}0.932{col 65}{space 4}-.4538168{col 78}{space 3} .4950033
{txt}{space 10}mv_unemployed {c |}{col 25}{res}{space 2}        0{col 37}{txt}  (omitted)
{space 8}mv_selfemployed {c |}{col 25}{res}{space 2}-.0855808{col 37}{space 2} .2488154{col 48}{space 1}   -0.34{col 57}{space 3}0.731{col 65}{space 4}-.5732762{col 78}{space 3} .4021147
{txt}{space 13}mv_outofLF {c |}{col 25}{res}{space 2}        0{col 37}{txt}  (omitted)
{space 10}mv_goodhealth {c |}{col 25}{res}{space 2}-.2201189{col 37}{space 2}  .204599{col 48}{space 1}   -1.08{col 57}{space 3}0.282{col 65}{space 4} -.621147{col 78}{space 3} .1809093
{txt}{space 15}mv_white {c |}{col 25}{res}{space 2}-.4414172{col 37}{space 2} .3302877{col 48}{space 1}   -1.34{col 57}{space 3}0.181{col 65}{space 4}-1.088804{col 78}{space 3} .2059696
{txt}{space 15}mv_black {c |}{col 25}{res}{space 2}        0{col 37}{txt}  (omitted)
{space 14}mv_latino {c |}{col 25}{res}{space 2}        0{col 37}{txt}  (omitted)
{space 15}mv_asian {c |}{col 25}{res}{space 2}        0{col 37}{txt}  (omitted)
{space 9}mv_whitecollar {c |}{col 25}{res}{space 2}-.2186681{col 37}{space 2}  .254783{col 48}{space 1}   -0.86{col 57}{space 3}0.391{col 65}{space 4}-.7180605{col 78}{space 3} .2807244
{txt}{space 10}mv_bluecollar {c |}{col 25}{res}{space 2}        0{col 37}{txt}  (omitted)
{space 7}mv_serviceworker {c |}{col 25}{res}{space 2}        0{col 37}{txt}  (omitted)
{space 23} {c |}
{space 16}country {c |}
{space 15}Austria  {c |}{col 25}{res}{space 2}-.3741934{col 37}{space 2} .4383146{col 48}{space 1}   -0.85{col 57}{space 3}0.393{col 65}{space 4}-1.233321{col 78}{space 3} .4849337
{txt}{space 16}Brazil  {c |}{col 25}{res}{space 2}-.3597827{col 37}{space 2} .0576422{col 48}{space 1}   -6.24{col 57}{space 3}0.000{col 65}{space 4}-.4727654{col 78}{space 3}-.2467999
{txt}{space 16}France  {c |}{col 25}{res}{space 2}        0{col 37}{txt}  (omitted)
{space 15}Germany  {c |}{col 25}{res}{space 2}-.5538239{col 37}{space 2} .4690035{col 48}{space 1}   -1.18{col 57}{space 3}0.238{col 65}{space 4}-1.473103{col 78}{space 3} .3654555
{txt}{space 17}Italy  {c |}{col 25}{res}{space 2}-.7833822{col 37}{space 2} .4246299{col 48}{space 1}   -1.84{col 57}{space 3}0.065{col 65}{space 4}-1.615686{col 78}{space 3} .0489219
{txt}{space 11}New_Zealand  {c |}{col 25}{res}{space 2}        0{col 37}{txt}  (omitted)
{space 16}Poland  {c |}{col 25}{res}{space 2}-.0815363{col 37}{space 2} .0609156{col 48}{space 1}   -1.34{col 57}{space 3}0.181{col 65}{space 4}-.2009352{col 78}{space 3} .0378625
{txt}{space 17}Spain  {c |}{col 25}{res}{space 2}        0{col 37}{txt}  (omitted)
{space 16}Sweden  {c |}{col 25}{res}{space 2} -.116286{col 37}{space 2} .0724859{col 48}{space 1}   -1.60{col 57}{space 3}0.109{col 65}{space 4}-.2583634{col 78}{space 3} .0257915
{txt}{space 20}UK  {c |}{col 25}{res}{space 2}        0{col 37}{txt}  (omitted)
{space 20}US  {c |}{col 25}{res}{space 2}        0{col 37}{txt}  (omitted)
{space 23} {c |}
{space 18}_cons {c |}{col 25}{res}{space 2}-.1788695{col 37}{space 2} .1261144{col 48}{space 1}   -1.42{col 57}{space 3}0.156{col 65}{space 4}-.4260624{col 78}{space 3} .0683235
{txt}{hline 24}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{help ivreg2##idtest:Underidentification test} (Kleibergen-Paap rk LM statistic):{res}{col 71}  10.045
{txt}{col 52}Chi-sq({res}1{txt}) P-val =  {res}{col 73}0.0015
{txt}{hline 78}
{help ivreg2##widtest:Weak identification test} (Cragg-Donald Wald F statistic):{res}{col 71}  10.009
{txt}                         (Kleibergen-Paap rk Wald F statistic):{res}{col 71}  10.040
{txt}Stock-Yogo weak ID test critical values:{res}{txt}{col 42}10% maximal IV size{res}{col 73} 16.38
{txt}{col 42}15% maximal IV size{res}{col 73}  8.96
{txt}{col 42}20% maximal IV size{res}{col 73}  6.66
{txt}{col 42}25% maximal IV size{res}{col 73}  5.53
{txt}Source: Stock-Yogo (2005).  Reproduced by permission.
NB: Critical values are for Cragg-Donald F statistic and i.i.d. errors.
{hline 78}
{help ivreg2##overidtests:Hansen J statistic} (overidentification test of all instruments):{res}{col 71}   0.000
{txt}{col 50}(equation exactly identified)
{hline 78}
Instrumented:{col 23}satis_head
Included instruments:{col 23}thirties fourties fifties sixties seventies
{col 23}income2quartile income3quartile income4quartile
{col 23}incomenoanswer female highschool college noreligion
{col 23}christiannotcatholic catholic fulltimeworker
{col 23}parttimeworker unemployed selfemployed outofLF goodhealth
{col 23}white black latino asian whitecollar bluecollar
{col 23}serviceworker mv_incomenoanswer mv_highschool
{col 23}mv_noreligion mv_parttimeworker mv_selfemployed
{col 23}mv_goodhealth mv_white mv_whitecollar 2.country 3.country
{col 23}5.country 6.country 8.country 10.country
Excluded instruments:{col 23}treatc
Dropped collinear:{col 23}mv_thirties mv_fourties mv_fifties mv_sixties
{col 23}mv_seventies mv_income2quartile mv_income3quartile
{col 23}mv_income4quartile mv_female mv_college
{col 23}mv_christiannotcatholic mv_catholic mv_fulltimeworker
{col 23}mv_unemployed mv_outofLF mv_black mv_latino mv_asian
{col 23}mv_bluecollar mv_serviceworker 4.country 7.country
{col 23}9.country 11.country 12.country
{hline 78}
({res}est6{txt} stored)

{com}. 
. estadd scalar fstat = e(widstat)

{txt}added scalar:
              e(fstat) =  {res}10.040317
{txt}
{com}. estadd local IV "SumIV", replace

{txt}added macro:
                 e(IV) : "{res:SumIV}"

{com}. estadd local Controls "X", replace

{txt}added macro:
           e(Controls) : "{res:X}"

{com}. estadd local CFE "X", replace

{txt}added macro:
                e(CFE) : "{res:X}"

{com}. get_mean

{txt}added scalar:
                 e(av) =  {res}0
{txt}
{com}. 
. esttab using "3_output/2_OA/Tables/TableE8.tex", depvar keep(satis_head) ///
>         label replace wrap ///
>         cells(b(star fmt(%15.3fc)) se(par fmt(%15.3fc))) interaction(" $/times$ ") ///
>         star(* 0.10 ** 0.05 *** 0.01) ///
>         stats(Controls CFE N av IV fstat , layout(@ @ @ @ @ @ ) fmt(%15s %15s %15.0fc %12.3f %9.3f %9.3f ) ///
>         labels("Individual controls" "Country FE" Observations "Outcome mean" "Instruments" "F-statistic" )) ////
>         collabels(none) mlabels(none) ///
>         mgroups("External efficacy" "Internal efficacy" "Standardized index" , pattern(1 0 1 0 1 0 ) ///
>         prefix(\multicolumn{c -(}@span{c )-}{c -(}c{c )-}{c -(}) suffix({c )-}) span erepeat(\cmidrule(lr){c -(}@span{c )-}))
{res}{txt}(output written to {browse  `"3_output/2_OA/Tables/TableE8.tex"'})

{com}. 
.         
. 
. 
. /* Cross-national heterogeneity, excluding young democracies */ 
. preserve
{txt}
{com}. 
. keep if country_str!= "Poland" & country_str!= "Brazil"
{txt}(2,002 observations deleted)

{com}. 
. **# Table E.9: Impact on satisfaction with democracy, excluding Brazil and Poland - 2SLS.
. /* E.9. Col 1: 16 instruments */
. eststo clear
{txt}
{com}. eststo: ivreg2 satis_dem ///
>         (satis_head = TH1_TE1 TH1_TE2 TH1_TE3 TH1_TE4 TH2_TE1 TH2_TE2 TH2_TE3 TH2_TE4 TH3_TE1 TH3_TE2 TH3_TE3 TH3_TE4 TH4_TE1 TH4_TE2 TH4_TE3), small robust 
{res}
{txt}IV (2SLS) estimation
{hline 20}

Estimates efficient for homoskedasticity only
Statistics robust to heteroskedasticity

{col 55}Number of obs = {res}   20539
{txt}{col 55}F(  1, 20537) = {res}   10.76
{txt}{col 55}Prob > F      = {res}  0.0010
{txt}Total (centered) SS     = {res}  1444.70131{txt}{col 55}Centered R2   = {res}  0.3865
{txt}Total (uncentered) SS   = {res} 6722.940042{txt}{col 55}Uncentered R2 = {res}  0.8682
{txt}Residual SS             = {res} 886.3678311{txt}{col 55}Root MSE      = {res}   .2077

{txt}{hline 13}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 14}{c |}{col 26}    Robust
{col 1}   satis_dem{col 14}{c |} Coefficient{col 26}  std. err.{col 38}      t{col 46}   P>|t|{col 54}     [95% con{col 67}f. interval]
{hline 13}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 2}satis_head {c |}{col 14}{res}{space 2} .4446429{col 26}{space 2} .1355789{col 37}{space 1}    3.28{col 46}{space 3}0.001{col 54}{space 4} .1788975{col 67}{space 3} .7103883
{txt}{space 7}_cons {c |}{col 14}{res}{space 2} .3008315{col 26}{space 2} .0628611{col 37}{space 1}    4.79{col 46}{space 3}0.000{col 54}{space 4} .1776187{col 67}{space 3} .4240443
{txt}{hline 13}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{help ivreg2##idtest:Underidentification test} (Kleibergen-Paap rk LM statistic):{res}{col 71}  24.373
{txt}{col 52}Chi-sq({res}15{txt}) P-val =  {res}{col 73}0.0590
{txt}{hline 78}
{help ivreg2##widtest:Weak identification test} (Cragg-Donald Wald F statistic):{res}{col 71}   1.620
{txt}                         (Kleibergen-Paap rk Wald F statistic):{res}{col 71}   1.631
{txt}Stock-Yogo weak ID test critical values:{res}{txt}{col 42} 5% maximal IV relative bias{res}{col 73} 21.23
{txt}{col 42}10% maximal IV relative bias{res}{col 73} 11.51
{txt}{col 42}20% maximal IV relative bias{res}{col 73}  6.42
{txt}{col 42}30% maximal IV relative bias{res}{col 73}  4.63
{txt}{col 42}10% maximal IV size{res}{col 73} 50.39
{txt}{col 42}15% maximal IV size{res}{col 73} 26.80
{txt}{col 42}20% maximal IV size{res}{col 73} 18.72
{txt}{col 42}25% maximal IV size{res}{col 73} 14.60
{txt}Source: Stock-Yogo (2005).  Reproduced by permission.
NB: Critical values are for Cragg-Donald F statistic and i.i.d. errors.
{hline 78}
{help ivreg2##overidtests:Hansen J statistic} (overidentification test of all instruments):{res}{col 71}  10.709
{txt}{col 52}Chi-sq({res}14{txt}) P-val =  {res}{col 73}0.7087
{txt}{hline 78}
Instrumented:{col 23}satis_head
Excluded instruments:{col 23}TH1_TE1 TH1_TE2 TH1_TE3 TH1_TE4 TH2_TE1 TH2_TE2 TH2_TE3
{col 23}TH2_TE4 TH3_TE1 TH3_TE2 TH3_TE3 TH3_TE4 TH4_TE1 TH4_TE2
{col 23}TH4_TE3
{hline 78}
({res}est1{txt} stored)

{com}. 
. estadd scalar fstat = e(widstat)

{txt}added scalar:
              e(fstat) =  {res}1.6310577
{txt}
{com}. estadd local IV "16 IVs"

{txt}added macro:
                 e(IV) : "{res:16 IVs}"

{com}. estadd local nothing nothing2 = " "

{txt}added macro:
            e(nothing) : "{res:nothing2 = " "}"

{com}. get_mean

{txt}added scalar:
                 e(av) =  {res}.507
{txt}
{com}. 
. /* E.9. Col 2: 16 instruments + controls */
. eststo: ivreg2 satis_dem ///
>         (satis_head = TH1_TE1 TH1_TE2 TH1_TE3 TH1_TE4 TH2_TE1 TH2_TE2 TH2_TE3 TH2_TE4 TH3_TE1 TH3_TE2 TH3_TE3 TH3_TE4 TH4_TE1 TH4_TE2 TH4_TE3) $controls $mv_controls i.country, small robust 
{txt}Warning - collinearities detected
Vars dropped:{col 21}mv_thirties mv_fourties mv_fifties mv_sixties mv_seventies
{col 21}mv_income2quartile mv_income3quartile mv_income4quartile
{col 21}mv_female mv_college mv_christiannotcatholic mv_catholic
{col 21}mv_fulltimeworker mv_unemployed mv_outofLF mv_black
{col 21}mv_latino mv_asian mv_bluecollar mv_serviceworker 4.country
{col 21}7.country 9.country 11.country 12.country
{res}
{txt}IV (2SLS) estimation
{hline 20}

Estimates efficient for homoskedasticity only
Statistics robust to heteroskedasticity

{col 55}Number of obs = {res}   20539
{txt}{col 55}F( 41, 20497) = {res}  109.39
{txt}{col 55}Prob > F      = {res}  0.0000
{txt}Total (centered) SS     = {res}  1444.70131{txt}{col 55}Centered R2   = {res}  0.4261
{txt}Total (uncentered) SS   = {res} 6722.940042{txt}{col 55}Uncentered R2 = {res}  0.8767
{txt}Residual SS             = {res} 829.1081907{txt}{col 55}Root MSE      = {res}   .2011

{txt}{hline 24}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 25}{c |}{col 37}    Robust
{col 1}              satis_dem{col 25}{c |} Coefficient{col 37}  std. err.{col 49}      t{col 57}   P>|t|{col 65}     [95% con{col 78}f. interval]
{hline 24}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 13}satis_head {c |}{col 25}{res}{space 2} .4446143{col 37}{space 2} .1300024{col 48}{space 1}    3.42{col 57}{space 3}0.001{col 65}{space 4} .1897993{col 78}{space 3} .6994293
{txt}{space 15}thirties {c |}{col 25}{res}{space 2}-.0190654{col 37}{space 2} .0060964{col 48}{space 1}   -3.13{col 57}{space 3}0.002{col 65}{space 4}-.0310147{col 78}{space 3} -.007116
{txt}{space 15}fourties {c |}{col 25}{res}{space 2}-.0166131{col 37}{space 2} .0078375{col 48}{space 1}   -2.12{col 57}{space 3}0.034{col 65}{space 4}-.0319753{col 78}{space 3}-.0012509
{txt}{space 16}fifties {c |}{col 25}{res}{space 2}-.0123905{col 37}{space 2} .0076599{col 48}{space 1}   -1.62{col 57}{space 3}0.106{col 65}{space 4}-.0274046{col 78}{space 3} .0026236
{txt}{space 16}sixties {c |}{col 25}{res}{space 2} .0028931{col 37}{space 2} .0077937{col 48}{space 1}    0.37{col 57}{space 3}0.710{col 65}{space 4}-.0123831{col 78}{space 3} .0181693
{txt}{space 14}seventies {c |}{col 25}{res}{space 2} .0218559{col 37}{space 2} .0069142{col 48}{space 1}    3.16{col 57}{space 3}0.002{col 65}{space 4} .0083035{col 78}{space 3} .0354082
{txt}{space 8}income2quartile {c |}{col 25}{res}{space 2} .0199889{col 37}{space 2} .0047687{col 48}{space 1}    4.19{col 57}{space 3}0.000{col 65}{space 4} .0106419{col 78}{space 3}  .029336
{txt}{space 8}income3quartile {c |}{col 25}{res}{space 2} .0286197{col 37}{space 2} .0055137{col 48}{space 1}    5.19{col 57}{space 3}0.000{col 65}{space 4} .0178125{col 78}{space 3} .0394269
{txt}{space 8}income4quartile {c |}{col 25}{res}{space 2} .0455032{col 37}{space 2} .0074243{col 48}{space 1}    6.13{col 57}{space 3}0.000{col 65}{space 4} .0309509{col 78}{space 3} .0600555
{txt}{space 9}incomenoanswer {c |}{col 25}{res}{space 2} .0053882{col 37}{space 2} .0071374{col 48}{space 1}    0.75{col 57}{space 3}0.450{col 65}{space 4}-.0086017{col 78}{space 3}  .019378
{txt}{space 17}female {c |}{col 25}{res}{space 2}-.0062136{col 37}{space 2} .0033406{col 48}{space 1}   -1.86{col 57}{space 3}0.063{col 65}{space 4}-.0127614{col 78}{space 3} .0003342
{txt}{space 13}highschool {c |}{col 25}{res}{space 2} .0045875{col 37}{space 2} .0047418{col 48}{space 1}    0.97{col 57}{space 3}0.333{col 65}{space 4}-.0047068{col 78}{space 3} .0138819
{txt}{space 16}college {c |}{col 25}{res}{space 2} .0274166{col 37}{space 2} .0044341{col 48}{space 1}    6.18{col 57}{space 3}0.000{col 65}{space 4} .0187254{col 78}{space 3} .0361078
{txt}{space 13}noreligion {c |}{col 25}{res}{space 2}-.0175687{col 37}{space 2} .0070402{col 48}{space 1}   -2.50{col 57}{space 3}0.013{col 65}{space 4}-.0313681{col 78}{space 3}-.0037693
{txt}{space 3}christiannotcatholic {c |}{col 25}{res}{space 2} .0140571{col 37}{space 2} .0106667{col 48}{space 1}    1.32{col 57}{space 3}0.188{col 65}{space 4}-.0068505{col 78}{space 3} .0349647
{txt}{space 15}catholic {c |}{col 25}{res}{space 2} .0118892{col 37}{space 2} .0072179{col 48}{space 1}    1.65{col 57}{space 3}0.100{col 65}{space 4}-.0022584{col 78}{space 3} .0260369
{txt}{space 9}fulltimeworker {c |}{col 25}{res}{space 2}-.0577762{col 37}{space 2} .0413143{col 48}{space 1}   -1.40{col 57}{space 3}0.162{col 65}{space 4}-.1387555{col 78}{space 3} .0232031
{txt}{space 9}parttimeworker {c |}{col 25}{res}{space 2}-.0314059{col 37}{space 2} .0435892{col 48}{space 1}   -0.72{col 57}{space 3}0.471{col 65}{space 4}-.1168442{col 78}{space 3} .0540323
{txt}{space 13}unemployed {c |}{col 25}{res}{space 2}-.0716816{col 37}{space 2} .0461941{col 48}{space 1}   -1.55{col 57}{space 3}0.121{col 65}{space 4}-.1622257{col 78}{space 3} .0188626
{txt}{space 11}selfemployed {c |}{col 25}{res}{space 2}-.0511611{col 37}{space 2} .0564227{col 48}{space 1}   -0.91{col 57}{space 3}0.365{col 65}{space 4}-.1617542{col 78}{space 3} .0594319
{txt}{space 16}outofLF {c |}{col 25}{res}{space 2}-.0604814{col 37}{space 2}  .043009{col 48}{space 1}   -1.41{col 57}{space 3}0.160{col 65}{space 4}-.1447823{col 78}{space 3} .0238196
{txt}{space 13}goodhealth {c |}{col 25}{res}{space 2} .0298648{col 37}{space 2} .0071296{col 48}{space 1}    4.19{col 57}{space 3}0.000{col 65}{space 4} .0158903{col 78}{space 3} .0438393
{txt}{space 18}white {c |}{col 25}{res}{space 2}-.0018222{col 37}{space 2} .0386271{col 48}{space 1}   -0.05{col 57}{space 3}0.962{col 65}{space 4}-.0775343{col 78}{space 3}   .07389
{txt}{space 18}black {c |}{col 25}{res}{space 2}  .066598{col 37}{space 2} .0378191{col 48}{space 1}    1.76{col 57}{space 3}0.078{col 65}{space 4}-.0075305{col 78}{space 3} .1407265
{txt}{space 17}latino {c |}{col 25}{res}{space 2} .0577534{col 37}{space 2} .0399174{col 48}{space 1}    1.45{col 57}{space 3}0.148{col 65}{space 4}-.0204879{col 78}{space 3} .1359947
{txt}{space 18}asian {c |}{col 25}{res}{space 2} .0390275{col 37}{space 2} .0401394{col 48}{space 1}    0.97{col 57}{space 3}0.331{col 65}{space 4} -.039649{col 78}{space 3}  .117704
{txt}{space 12}whitecollar {c |}{col 25}{res}{space 2} .0032505{col 37}{space 2}  .008405{col 48}{space 1}    0.39{col 57}{space 3}0.699{col 65}{space 4}-.0132241{col 78}{space 3}  .019725
{txt}{space 13}bluecollar {c |}{col 25}{res}{space 2}-.0206966{col 37}{space 2} .0110961{col 48}{space 1}   -1.87{col 57}{space 3}0.062{col 65}{space 4}-.0424459{col 78}{space 3} .0010526
{txt}{space 10}serviceworker {c |}{col 25}{res}{space 2}-.0078872{col 37}{space 2} .0070158{col 48}{space 1}   -1.12{col 57}{space 3}0.261{col 65}{space 4}-.0216387{col 78}{space 3} .0058643
{txt}{space 12}mv_thirties {c |}{col 25}{res}{space 2}        0{col 37}{txt}  (omitted)
{space 12}mv_fourties {c |}{col 25}{res}{space 2}        0{col 37}{txt}  (omitted)
{space 13}mv_fifties {c |}{col 25}{res}{space 2}        0{col 37}{txt}  (omitted)
{space 13}mv_sixties {c |}{col 25}{res}{space 2}        0{col 37}{txt}  (omitted)
{space 11}mv_seventies {c |}{col 25}{res}{space 2}        0{col 37}{txt}  (omitted)
{space 5}mv_income2quartile {c |}{col 25}{res}{space 2}        0{col 37}{txt}  (omitted)
{space 5}mv_income3quartile {c |}{col 25}{res}{space 2}        0{col 37}{txt}  (omitted)
{space 5}mv_income4quartile {c |}{col 25}{res}{space 2}        0{col 37}{txt}  (omitted)
{space 6}mv_incomenoanswer {c |}{col 25}{res}{space 2} .0233041{col 37}{space 2} .0127789{col 48}{space 1}    1.82{col 57}{space 3}0.068{col 65}{space 4}-.0017435{col 78}{space 3} .0483516
{txt}{space 14}mv_female {c |}{col 25}{res}{space 2}        0{col 37}{txt}  (omitted)
{space 10}mv_highschool {c |}{col 25}{res}{space 2} .0255246{col 37}{space 2} .0108663{col 48}{space 1}    2.35{col 57}{space 3}0.019{col 65}{space 4} .0042258{col 78}{space 3} .0468234
{txt}{space 13}mv_college {c |}{col 25}{res}{space 2}        0{col 37}{txt}  (omitted)
{space 10}mv_noreligion {c |}{col 25}{res}{space 2}-.0032199{col 37}{space 2} .0230889{col 48}{space 1}   -0.14{col 57}{space 3}0.889{col 65}{space 4}-.0484759{col 78}{space 3} .0420362
{txt}mv_christiannotcatholic {c |}{col 25}{res}{space 2}        0{col 37}{txt}  (omitted)
{space 12}mv_catholic {c |}{col 25}{res}{space 2}        0{col 37}{txt}  (omitted)
{space 6}mv_fulltimeworker {c |}{col 25}{res}{space 2}        0{col 37}{txt}  (omitted)
{space 6}mv_parttimeworker {c |}{col 25}{res}{space 2}-.0390235{col 37}{space 2} .0401797{col 48}{space 1}   -0.97{col 57}{space 3}0.331{col 65}{space 4}-.1177789{col 78}{space 3}  .039732
{txt}{space 10}mv_unemployed {c |}{col 25}{res}{space 2}        0{col 37}{txt}  (omitted)
{space 8}mv_selfemployed {c |}{col 25}{res}{space 2} .0534234{col 37}{space 2} .0414708{col 48}{space 1}    1.29{col 57}{space 3}0.198{col 65}{space 4}-.0278625{col 78}{space 3} .1347094
{txt}{space 13}mv_outofLF {c |}{col 25}{res}{space 2}        0{col 37}{txt}  (omitted)
{space 10}mv_goodhealth {c |}{col 25}{res}{space 2} .0932947{col 37}{space 2} .0735391{col 48}{space 1}    1.27{col 57}{space 3}0.205{col 65}{space 4}-.0508478{col 78}{space 3} .2374372
{txt}{space 15}mv_white {c |}{col 25}{res}{space 2} .0606198{col 37}{space 2} .0600505{col 48}{space 1}    1.01{col 57}{space 3}0.313{col 65}{space 4}-.0570839{col 78}{space 3} .1783235
{txt}{space 15}mv_black {c |}{col 25}{res}{space 2}        0{col 37}{txt}  (omitted)
{space 14}mv_latino {c |}{col 25}{res}{space 2}        0{col 37}{txt}  (omitted)
{space 15}mv_asian {c |}{col 25}{res}{space 2}        0{col 37}{txt}  (omitted)
{space 9}mv_whitecollar {c |}{col 25}{res}{space 2} .0013491{col 37}{space 2} .0410586{col 48}{space 1}    0.03{col 57}{space 3}0.974{col 65}{space 4} -.079129{col 78}{space 3} .0818273
{txt}{space 10}mv_bluecollar {c |}{col 25}{res}{space 2}        0{col 37}{txt}  (omitted)
{space 7}mv_serviceworker {c |}{col 25}{res}{space 2}        0{col 37}{txt}  (omitted)
{space 23} {c |}
{space 16}country {c |}
{space 15}Austria  {c |}{col 25}{res}{space 2} .0838347{col 37}{space 2} .0765157{col 48}{space 1}    1.10{col 57}{space 3}0.273{col 65}{space 4}-.0661423{col 78}{space 3} .2338116
{txt}{space 16}France  {c |}{col 25}{res}{space 2}        0{col 37}{txt}  (omitted)
{space 15}Germany  {c |}{col 25}{res}{space 2} .0655824{col 37}{space 2} .0813326{col 48}{space 1}    0.81{col 57}{space 3}0.420{col 65}{space 4}-.0938361{col 78}{space 3} .2250008
{txt}{space 17}Italy  {c |}{col 25}{res}{space 2}-.0304918{col 37}{space 2} .0767521{col 48}{space 1}   -0.40{col 57}{space 3}0.691{col 65}{space 4} -.180932{col 78}{space 3} .1199485
{txt}{space 11}New_Zealand  {c |}{col 25}{res}{space 2}        0{col 37}{txt}  (omitted)
{space 17}Spain  {c |}{col 25}{res}{space 2}        0{col 37}{txt}  (omitted)
{space 16}Sweden  {c |}{col 25}{res}{space 2} .0661433{col 37}{space 2} .0159578{col 48}{space 1}    4.14{col 57}{space 3}0.000{col 65}{space 4} .0348647{col 78}{space 3} .0974219
{txt}{space 20}UK  {c |}{col 25}{res}{space 2}        0{col 37}{txt}  (omitted)
{space 20}US  {c |}{col 25}{res}{space 2}        0{col 37}{txt}  (omitted)
{space 23} {c |}
{space 18}_cons {c |}{col 25}{res}{space 2} .2105556{col 37}{space 2} .0381255{col 48}{space 1}    5.52{col 57}{space 3}0.000{col 65}{space 4} .1358266{col 78}{space 3} .2852846
{txt}{hline 24}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{help ivreg2##idtest:Underidentification test} (Kleibergen-Paap rk LM statistic):{res}{col 71}  28.748
{txt}{col 52}Chi-sq({res}15{txt}) P-val =  {res}{col 73}0.0173
{txt}{hline 78}
{help ivreg2##widtest:Weak identification test} (Cragg-Donald Wald F statistic):{res}{col 71}   1.881
{txt}                         (Kleibergen-Paap rk Wald F statistic):{res}{col 71}   1.921
{txt}Stock-Yogo weak ID test critical values:{res}{txt}{col 42} 5% maximal IV relative bias{res}{col 73} 21.23
{txt}{col 42}10% maximal IV relative bias{res}{col 73} 11.51
{txt}{col 42}20% maximal IV relative bias{res}{col 73}  6.42
{txt}{col 42}30% maximal IV relative bias{res}{col 73}  4.63
{txt}{col 42}10% maximal IV size{res}{col 73} 50.39
{txt}{col 42}15% maximal IV size{res}{col 73} 26.80
{txt}{col 42}20% maximal IV size{res}{col 73} 18.72
{txt}{col 42}25% maximal IV size{res}{col 73} 14.60
{txt}Source: Stock-Yogo (2005).  Reproduced by permission.
NB: Critical values are for Cragg-Donald F statistic and i.i.d. errors.
{hline 78}
{help ivreg2##overidtests:Hansen J statistic} (overidentification test of all instruments):{res}{col 71}  10.510
{txt}{col 52}Chi-sq({res}14{txt}) P-val =  {res}{col 73}0.7240
{txt}{hline 78}
Instrumented:{col 23}satis_head
Included instruments:{col 23}thirties fourties fifties sixties seventies
{col 23}income2quartile income3quartile income4quartile
{col 23}incomenoanswer female highschool college noreligion
{col 23}christiannotcatholic catholic fulltimeworker
{col 23}parttimeworker unemployed selfemployed outofLF goodhealth
{col 23}white black latino asian whitecollar bluecollar
{col 23}serviceworker mv_incomenoanswer mv_highschool
{col 23}mv_noreligion mv_parttimeworker mv_selfemployed
{col 23}mv_goodhealth mv_white mv_whitecollar 2.country 5.country
{col 23}6.country 10.country
Excluded instruments:{col 23}TH1_TE1 TH1_TE2 TH1_TE3 TH1_TE4 TH2_TE1 TH2_TE2 TH2_TE3
{col 23}TH2_TE4 TH3_TE1 TH3_TE2 TH3_TE3 TH3_TE4 TH4_TE1 TH4_TE2
{col 23}TH4_TE3
Dropped collinear:{col 23}mv_thirties mv_fourties mv_fifties mv_sixties
{col 23}mv_seventies mv_income2quartile mv_income3quartile
{col 23}mv_income4quartile mv_female mv_college
{col 23}mv_christiannotcatholic mv_catholic mv_fulltimeworker
{col 23}mv_unemployed mv_outofLF mv_black mv_latino mv_asian
{col 23}mv_bluecollar mv_serviceworker 4.country 7.country
{col 23}9.country 11.country 12.country
{hline 78}
({res}est2{txt} stored)

{com}.         
. estadd scalar fstat = e(widstat)

{txt}added scalar:
              e(fstat) =  {res}1.9211928
{txt}
{com}. estadd local CFE "X", replace

{txt}added macro:
                e(CFE) : "{res:X}"

{com}. estadd local Controls "X", replace

{txt}added macro:
           e(Controls) : "{res:X}"

{com}. estadd local IV "16 IVs"

{txt}added macro:
                 e(IV) : "{res:16 IVs}"

{com}. estadd local nothing nothing2 = " "

{txt}added macro:
            e(nothing) : "{res:nothing2 = " "}"

{com}. get_mean

{txt}added scalar:
                 e(av) =  {res}.507
{txt}
{com}.         
. /* E.9. Col 3: 2 instruments */ 
. eststo: ivreg2 satis_dem ///
>         (satis_head = healthc econc), small robust 
{res}
{txt}IV (2SLS) estimation
{hline 20}

Estimates efficient for homoskedasticity only
Statistics robust to heteroskedasticity

{col 55}Number of obs = {res}   20539
{txt}{col 55}F(  1, 20537) = {res}    5.23
{txt}{col 55}Prob > F      = {res}  0.0222
{txt}Total (centered) SS     = {res}  1444.70131{txt}{col 55}Centered R2   = {res}  0.3848
{txt}Total (uncentered) SS   = {res} 6722.940042{txt}{col 55}Uncentered R2 = {res}  0.8678
{txt}Residual SS             = {res} 888.7379656{txt}{col 55}Root MSE      = {res}    .208

{txt}{hline 13}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 14}{c |}{col 26}    Robust
{col 1}   satis_dem{col 14}{c |} Coefficient{col 26}  std. err.{col 38}      t{col 46}   P>|t|{col 54}     [95% con{col 67}f. interval]
{hline 13}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 2}satis_head {c |}{col 14}{res}{space 2} .4385597{col 26}{space 2} .1917786{col 37}{space 1}    2.29{col 46}{space 3}0.022{col 54}{space 4} .0626583{col 67}{space 3}  .814461
{txt}{space 7}_cons {c |}{col 14}{res}{space 2} .3036512{col 26}{space 2} .0889014{col 37}{space 1}    3.42{col 46}{space 3}0.001{col 54}{space 4} .1293974{col 67}{space 3} .4779051
{txt}{hline 13}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{help ivreg2##idtest:Underidentification test} (Kleibergen-Paap rk LM statistic):{res}{col 71}  12.165
{txt}{col 52}Chi-sq({res}2{txt}) P-val =  {res}{col 73}0.0023
{txt}{hline 78}
{help ivreg2##widtest:Weak identification test} (Cragg-Donald Wald F statistic):{res}{col 71}   6.081
{txt}                         (Kleibergen-Paap rk Wald F statistic):{res}{col 71}   6.091
{txt}Stock-Yogo weak ID test critical values:{res}{txt}{col 42}10% maximal IV size{res}{col 73} 19.93
{txt}{col 42}15% maximal IV size{res}{col 73} 11.59
{txt}{col 42}20% maximal IV size{res}{col 73}  8.75
{txt}{col 42}25% maximal IV size{res}{col 73}  7.25
{txt}Source: Stock-Yogo (2005).  Reproduced by permission.
NB: Critical values are for Cragg-Donald F statistic and i.i.d. errors.
{hline 78}
{help ivreg2##overidtests:Hansen J statistic} (overidentification test of all instruments):{res}{col 71}   0.258
{txt}{col 52}Chi-sq({res}1{txt}) P-val =  {res}{col 73}0.6117
{txt}{hline 78}
Instrumented:{col 23}satis_head
Excluded instruments:{col 23}healthc econc
{hline 78}
({res}est3{txt} stored)

{com}. 
. estadd scalar fstat = e(widstat)

{txt}added scalar:
              e(fstat) =  {res}6.0914361
{txt}
{com}. estadd local IV "2 SumIVs"

{txt}added macro:
                 e(IV) : "{res:2 SumIVs}"

{com}. get_mean

{txt}added scalar:
                 e(av) =  {res}.507
{txt}
{com}. 
. /* E.9. Col 4: 2 instruments + controls */      
. eststo: ivreg2 satis_dem ///
>         (satis_head = healthc econc) $controls $mv_controls i.country, small robust 
{txt}Warning - collinearities detected
Vars dropped:{col 21}mv_thirties mv_fourties mv_fifties mv_sixties mv_seventies
{col 21}mv_income2quartile mv_income3quartile mv_income4quartile
{col 21}mv_female mv_college mv_christiannotcatholic mv_catholic
{col 21}mv_fulltimeworker mv_unemployed mv_outofLF mv_black
{col 21}mv_latino mv_asian mv_bluecollar mv_serviceworker 4.country
{col 21}7.country 9.country 11.country 12.country
{res}
{txt}IV (2SLS) estimation
{hline 20}

Estimates efficient for homoskedasticity only
Statistics robust to heteroskedasticity

{col 55}Number of obs = {res}   20539
{txt}{col 55}F( 41, 20497) = {res}  109.85
{txt}{col 55}Prob > F      = {res}  0.0000
{txt}Total (centered) SS     = {res}  1444.70131{txt}{col 55}Centered R2   = {res}  0.4286
{txt}Total (uncentered) SS   = {res} 6722.940042{txt}{col 55}Uncentered R2 = {res}  0.8772
{txt}Residual SS             = {res} 825.5519738{txt}{col 55}Root MSE      = {res}   .2007

{txt}{hline 24}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 25}{c |}{col 37}    Robust
{col 1}              satis_dem{col 25}{c |} Coefficient{col 37}  std. err.{col 49}      t{col 57}   P>|t|{col 65}     [95% con{col 78}f. interval]
{hline 24}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 13}satis_head {c |}{col 25}{res}{space 2} .4605929{col 37}{space 2} .1833287{col 48}{space 1}    2.51{col 57}{space 3}0.012{col 65}{space 4} .1012541{col 78}{space 3} .8199317
{txt}{space 15}thirties {c |}{col 25}{res}{space 2}-.0186219{col 37}{space 2} .0070848{col 48}{space 1}   -2.63{col 57}{space 3}0.009{col 65}{space 4}-.0325086{col 78}{space 3}-.0047352
{txt}{space 15}fourties {c |}{col 25}{res}{space 2}-.0158609{col 37}{space 2} .0098607{col 48}{space 1}   -1.61{col 57}{space 3}0.108{col 65}{space 4}-.0351886{col 78}{space 3} .0034668
{txt}{space 16}fifties {c |}{col 25}{res}{space 2}-.0116878{col 37}{space 2} .0095787{col 48}{space 1}   -1.22{col 57}{space 3}0.222{col 65}{space 4}-.0304627{col 78}{space 3} .0070871
{txt}{space 16}sixties {c |}{col 25}{res}{space 2} .0036036{col 37}{space 2} .0097022{col 48}{space 1}    0.37{col 57}{space 3}0.710{col 65}{space 4}-.0154134{col 78}{space 3} .0226206
{txt}{space 14}seventies {c |}{col 25}{res}{space 2} .0222407{col 37}{space 2} .0075935{col 48}{space 1}    2.93{col 57}{space 3}0.003{col 65}{space 4} .0073568{col 78}{space 3} .0371246
{txt}{space 8}income2quartile {c |}{col 25}{res}{space 2} .0197048{col 37}{space 2} .0052744{col 48}{space 1}    3.74{col 57}{space 3}0.000{col 65}{space 4} .0093666{col 78}{space 3} .0300429
{txt}{space 8}income3quartile {c |}{col 25}{res}{space 2}  .028197{col 37}{space 2} .0064687{col 48}{space 1}    4.36{col 57}{space 3}0.000{col 65}{space 4} .0155177{col 78}{space 3} .0408762
{txt}{space 8}income4quartile {c |}{col 25}{res}{space 2} .0447705{col 37}{space 2} .0094547{col 48}{space 1}    4.74{col 57}{space 3}0.000{col 65}{space 4} .0262386{col 78}{space 3} .0633025
{txt}{space 9}incomenoanswer {c |}{col 25}{res}{space 2}   .00553{col 37}{space 2} .0072255{col 48}{space 1}    0.77{col 57}{space 3}0.444{col 65}{space 4}-.0086327{col 78}{space 3} .0196926
{txt}{space 17}female {c |}{col 25}{res}{space 2}-.0064262{col 37}{space 2} .0037571{col 48}{space 1}   -1.71{col 57}{space 3}0.087{col 65}{space 4}-.0137903{col 78}{space 3}  .000938
{txt}{space 13}highschool {c |}{col 25}{res}{space 2} .0045122{col 37}{space 2} .0047708{col 48}{space 1}    0.95{col 57}{space 3}0.344{col 65}{space 4} -.004839{col 78}{space 3} .0138633
{txt}{space 16}college {c |}{col 25}{res}{space 2} .0273241{col 37}{space 2} .0044808{col 48}{space 1}    6.10{col 57}{space 3}0.000{col 65}{space 4} .0185414{col 78}{space 3} .0361067
{txt}{space 13}noreligion {c |}{col 25}{res}{space 2}-.0171926{col 37}{space 2}  .007683{col 48}{space 1}   -2.24{col 57}{space 3}0.025{col 65}{space 4}-.0322518{col 78}{space 3}-.0021334
{txt}{space 3}christiannotcatholic {c |}{col 25}{res}{space 2} .0131057{col 37}{space 2} .0131542{col 48}{space 1}    1.00{col 57}{space 3}0.319{col 65}{space 4}-.0126777{col 78}{space 3}  .038889
{txt}{space 15}catholic {c |}{col 25}{res}{space 2} .0114978{col 37}{space 2} .0078365{col 48}{space 1}    1.47{col 57}{space 3}0.142{col 65}{space 4}-.0038625{col 78}{space 3}  .026858
{txt}{space 9}fulltimeworker {c |}{col 25}{res}{space 2}-.0527645{col 37}{space 2} .0578553{col 48}{space 1}   -0.91{col 57}{space 3}0.362{col 65}{space 4}-.1661655{col 78}{space 3} .0606364
{txt}{space 9}parttimeworker {c |}{col 25}{res}{space 2}-.0264958{col 37}{space 2} .0590773{col 48}{space 1}   -0.45{col 57}{space 3}0.654{col 65}{space 4}-.1422921{col 78}{space 3} .0893004
{txt}{space 13}unemployed {c |}{col 25}{res}{space 2}-.0661549{col 37}{space 2} .0642557{col 48}{space 1}   -1.03{col 57}{space 3}0.303{col 65}{space 4}-.1921013{col 78}{space 3} .0597914
{txt}{space 11}selfemployed {c |}{col 25}{res}{space 2}-.0452603{col 37}{space 2} .0740609{col 48}{space 1}   -0.61{col 57}{space 3}0.541{col 65}{space 4}-.1904255{col 78}{space 3}  .099905
{txt}{space 16}outofLF {c |}{col 25}{res}{space 2}-.0552595{col 37}{space 2} .0602321{col 48}{space 1}   -0.92{col 57}{space 3}0.359{col 65}{space 4}-.1733192{col 78}{space 3} .0628002
{txt}{space 13}goodhealth {c |}{col 25}{res}{space 2} .0290682{col 37}{space 2}  .009589{col 48}{space 1}    3.03{col 57}{space 3}0.002{col 65}{space 4} .0102731{col 78}{space 3} .0478633
{txt}{space 18}white {c |}{col 25}{res}{space 2}-.0039424{col 37}{space 2} .0426379{col 48}{space 1}   -0.09{col 57}{space 3}0.926{col 65}{space 4} -.087516{col 78}{space 3} .0796313
{txt}{space 18}black {c |}{col 25}{res}{space 2} .0663859{col 37}{space 2} .0380172{col 48}{space 1}    1.75{col 57}{space 3}0.081{col 65}{space 4}-.0081308{col 78}{space 3} .1409027
{txt}{space 17}latino {c |}{col 25}{res}{space 2} .0572097{col 37}{space 2} .0403223{col 48}{space 1}    1.42{col 57}{space 3}0.156{col 65}{space 4}-.0218252{col 78}{space 3} .1362445
{txt}{space 18}asian {c |}{col 25}{res}{space 2} .0379017{col 37}{space 2} .0413939{col 48}{space 1}    0.92{col 57}{space 3}0.360{col 65}{space 4}-.0432335{col 78}{space 3}  .119037
{txt}{space 12}whitecollar {c |}{col 25}{res}{space 2}  .003562{col 37}{space 2} .0087642{col 48}{space 1}    0.41{col 57}{space 3}0.684{col 65}{space 4}-.0136164{col 78}{space 3} .0207405
{txt}{space 13}bluecollar {c |}{col 25}{res}{space 2}-.0198215{col 37}{space 2} .0131433{col 48}{space 1}   -1.51{col 57}{space 3}0.132{col 65}{space 4}-.0455834{col 78}{space 3} .0059403
{txt}{space 10}serviceworker {c |}{col 25}{res}{space 2}-.0075314{col 37}{space 2} .0075779{col 48}{space 1}   -0.99{col 57}{space 3}0.320{col 65}{space 4}-.0223848{col 78}{space 3} .0073219
{txt}{space 12}mv_thirties {c |}{col 25}{res}{space 2}        0{col 37}{txt}  (omitted)
{space 12}mv_fourties {c |}{col 25}{res}{space 2}        0{col 37}{txt}  (omitted)
{space 13}mv_fifties {c |}{col 25}{res}{space 2}        0{col 37}{txt}  (omitted)
{space 13}mv_sixties {c |}{col 25}{res}{space 2}        0{col 37}{txt}  (omitted)
{space 11}mv_seventies {c |}{col 25}{res}{space 2}        0{col 37}{txt}  (omitted)
{space 5}mv_income2quartile {c |}{col 25}{res}{space 2}        0{col 37}{txt}  (omitted)
{space 5}mv_income3quartile {c |}{col 25}{res}{space 2}        0{col 37}{txt}  (omitted)
{space 5}mv_income4quartile {c |}{col 25}{res}{space 2}        0{col 37}{txt}  (omitted)
{space 6}mv_incomenoanswer {c |}{col 25}{res}{space 2} .0244756{col 37}{space 2} .0158941{col 48}{space 1}    1.54{col 57}{space 3}0.124{col 65}{space 4}-.0066781{col 78}{space 3} .0556293
{txt}{space 14}mv_female {c |}{col 25}{res}{space 2}        0{col 37}{txt}  (omitted)
{space 10}mv_highschool {c |}{col 25}{res}{space 2} .0256816{col 37}{space 2} .0108782{col 48}{space 1}    2.36{col 57}{space 3}0.018{col 65}{space 4} .0043595{col 78}{space 3} .0470037
{txt}{space 13}mv_college {c |}{col 25}{res}{space 2}        0{col 37}{txt}  (omitted)
{space 10}mv_noreligion {c |}{col 25}{res}{space 2}-.0033812{col 37}{space 2}  .023162{col 48}{space 1}   -0.15{col 57}{space 3}0.884{col 65}{space 4}-.0487805{col 78}{space 3} .0420182
{txt}mv_christiannotcatholic {c |}{col 25}{res}{space 2}        0{col 37}{txt}  (omitted)
{space 12}mv_catholic {c |}{col 25}{res}{space 2}        0{col 37}{txt}  (omitted)
{space 6}mv_fulltimeworker {c |}{col 25}{res}{space 2}        0{col 37}{txt}  (omitted)
{space 6}mv_parttimeworker {c |}{col 25}{res}{space 2}-.0343897{col 37}{space 2} .0550244{col 48}{space 1}   -0.62{col 57}{space 3}0.532{col 65}{space 4}-.1422419{col 78}{space 3} .0734624
{txt}{space 10}mv_unemployed {c |}{col 25}{res}{space 2}        0{col 37}{txt}  (omitted)
{space 8}mv_selfemployed {c |}{col 25}{res}{space 2} .0486768{col 37}{space 2} .0565323{col 48}{space 1}    0.86{col 57}{space 3}0.389{col 65}{space 4} -.062131{col 78}{space 3} .1594847
{txt}{space 13}mv_outofLF {c |}{col 25}{res}{space 2}        0{col 37}{txt}  (omitted)
{space 10}mv_goodhealth {c |}{col 25}{res}{space 2} .0902966{col 37}{space 2} .0767854{col 48}{space 1}    1.18{col 57}{space 3}0.240{col 65}{space 4} -.060209{col 78}{space 3} .2408021
{txt}{space 15}mv_white {c |}{col 25}{res}{space 2} .0545516{col 37}{space 2} .0779787{col 48}{space 1}    0.70{col 57}{space 3}0.484{col 65}{space 4}-.0982928{col 78}{space 3}  .207396
{txt}{space 15}mv_black {c |}{col 25}{res}{space 2}        0{col 37}{txt}  (omitted)
{space 14}mv_latino {c |}{col 25}{res}{space 2}        0{col 37}{txt}  (omitted)
{space 15}mv_asian {c |}{col 25}{res}{space 2}        0{col 37}{txt}  (omitted)
{space 9}mv_whitecollar {c |}{col 25}{res}{space 2}-.0033769{col 37}{space 2} .0561379{col 48}{space 1}   -0.06{col 57}{space 3}0.952{col 65}{space 4}-.1134117{col 78}{space 3} .1066579
{txt}{space 10}mv_bluecollar {c |}{col 25}{res}{space 2}        0{col 37}{txt}  (omitted)
{space 7}mv_serviceworker {c |}{col 25}{res}{space 2}        0{col 37}{txt}  (omitted)
{space 23} {c |}
{space 16}country {c |}
{space 15}Austria  {c |}{col 25}{res}{space 2} .0756107{col 37}{space 2} .1018591{col 48}{space 1}    0.74{col 57}{space 3}0.458{col 65}{space 4}-.1240412{col 78}{space 3} .2752626
{txt}{space 16}France  {c |}{col 25}{res}{space 2}        0{col 37}{txt}  (omitted)
{space 15}Germany  {c |}{col 25}{res}{space 2} .0566774{col 37}{space 2} .1091342{col 48}{space 1}    0.52{col 57}{space 3}0.604{col 65}{space 4}-.1572344{col 78}{space 3} .2705891
{txt}{space 17}Italy  {c |}{col 25}{res}{space 2}-.0382901{col 37}{space 2} .0997689{col 48}{space 1}   -0.38{col 57}{space 3}0.701{col 65}{space 4} -.233845{col 78}{space 3} .1572649
{txt}{space 11}New_Zealand  {c |}{col 25}{res}{space 2}        0{col 37}{txt}  (omitted)
{space 17}Spain  {c |}{col 25}{res}{space 2}        0{col 37}{txt}  (omitted)
{space 16}Sweden  {c |}{col 25}{res}{space 2} .0652366{col 37}{space 2} .0175455{col 48}{space 1}    3.72{col 57}{space 3}0.000{col 65}{space 4}  .030846{col 78}{space 3} .0996271
{txt}{space 20}UK  {c |}{col 25}{res}{space 2}        0{col 37}{txt}  (omitted)
{space 20}US  {c |}{col 25}{res}{space 2}        0{col 37}{txt}  (omitted)
{space 23} {c |}
{space 18}_cons {c |}{col 25}{res}{space 2} .2104831{col 37}{space 2} .0382153{col 48}{space 1}    5.51{col 57}{space 3}0.000{col 65}{space 4} .1355781{col 78}{space 3} .2853881
{txt}{hline 24}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{help ivreg2##idtest:Underidentification test} (Kleibergen-Paap rk LM statistic):{res}{col 71}  14.216
{txt}{col 52}Chi-sq({res}2{txt}) P-val =  {res}{col 73}0.0008
{txt}{hline 78}
{help ivreg2##widtest:Weak identification test} (Cragg-Donald Wald F statistic):{res}{col 71}   7.071
{txt}                         (Kleibergen-Paap rk Wald F statistic):{res}{col 71}   7.107
{txt}Stock-Yogo weak ID test critical values:{res}{txt}{col 42}10% maximal IV size{res}{col 73} 19.93
{txt}{col 42}15% maximal IV size{res}{col 73} 11.59
{txt}{col 42}20% maximal IV size{res}{col 73}  8.75
{txt}{col 42}25% maximal IV size{res}{col 73}  7.25
{txt}Source: Stock-Yogo (2005).  Reproduced by permission.
NB: Critical values are for Cragg-Donald F statistic and i.i.d. errors.
{hline 78}
{help ivreg2##overidtests:Hansen J statistic} (overidentification test of all instruments):{res}{col 71}   0.438
{txt}{col 52}Chi-sq({res}1{txt}) P-val =  {res}{col 73}0.5079
{txt}{hline 78}
Instrumented:{col 23}satis_head
Included instruments:{col 23}thirties fourties fifties sixties seventies
{col 23}income2quartile income3quartile income4quartile
{col 23}incomenoanswer female highschool college noreligion
{col 23}christiannotcatholic catholic fulltimeworker
{col 23}parttimeworker unemployed selfemployed outofLF goodhealth
{col 23}white black latino asian whitecollar bluecollar
{col 23}serviceworker mv_incomenoanswer mv_highschool
{col 23}mv_noreligion mv_parttimeworker mv_selfemployed
{col 23}mv_goodhealth mv_white mv_whitecollar 2.country 5.country
{col 23}6.country 10.country
Excluded instruments:{col 23}healthc econc
Dropped collinear:{col 23}mv_thirties mv_fourties mv_fifties mv_sixties
{col 23}mv_seventies mv_income2quartile mv_income3quartile
{col 23}mv_income4quartile mv_female mv_college
{col 23}mv_christiannotcatholic mv_catholic mv_fulltimeworker
{col 23}mv_unemployed mv_outofLF mv_black mv_latino mv_asian
{col 23}mv_bluecollar mv_serviceworker 4.country 7.country
{col 23}9.country 11.country 12.country
{hline 78}
({res}est4{txt} stored)

{com}. 
. estadd scalar fstat = e(widstat)

{txt}added scalar:
              e(fstat) =  {res}7.1068294
{txt}
{com}. estadd local IV "2 SumIVs"

{txt}added macro:
                 e(IV) : "{res:2 SumIVs}"

{com}. estadd local Controls "X", replace

{txt}added macro:
           e(Controls) : "{res:X}"

{com}. estadd local CFE "X", replace

{txt}added macro:
                e(CFE) : "{res:X}"

{com}. get_mean

{txt}added scalar:
                 e(av) =  {res}.507
{txt}
{com}. 
. /* E.9. Col 5: 1 instrument */  
. eststo: ivreg2 satis_dem ///
>         (satis_head = treatc), small robust 
{res}
{txt}IV (2SLS) estimation
{hline 20}

Estimates efficient for homoskedasticity only
Statistics robust to heteroskedasticity

{col 55}Number of obs = {res}   20539
{txt}{col 55}F(  1, 20537) = {res}    3.40
{txt}{col 55}Prob > F      = {res}  0.0652
{txt}Total (centered) SS     = {res}  1444.70131{txt}{col 55}Centered R2   = {res}  0.3696
{txt}Total (uncentered) SS   = {res} 6722.940042{txt}{col 55}Uncentered R2 = {res}  0.8645
{txt}Residual SS             = {res} 910.6715601{txt}{col 55}Root MSE      = {res}   .2106

{txt}{hline 13}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 14}{c |}{col 26}    Robust
{col 1}   satis_dem{col 14}{c |} Coefficient{col 26}  std. err.{col 38}      t{col 46}   P>|t|{col 54}     [95% con{col 67}f. interval]
{hline 13}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 2}satis_head {c |}{col 14}{res}{space 2} .3937995{col 26}{space 2} .2135613{col 37}{space 1}    1.84{col 46}{space 3}0.065{col 54}{space 4}-.0247975{col 67}{space 3} .8123966
{txt}{space 7}_cons {c |}{col 14}{res}{space 2}  .324399{col 26}{space 2} .0989999{col 37}{space 1}    3.28{col 46}{space 3}0.001{col 54}{space 4} .1303513{col 67}{space 3} .5184468
{txt}{hline 13}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{help ivreg2##idtest:Underidentification test} (Kleibergen-Paap rk LM statistic):{res}{col 71}  10.027
{txt}{col 52}Chi-sq({res}1{txt}) P-val =  {res}{col 73}0.0015
{txt}{hline 78}
{help ivreg2##widtest:Weak identification test} (Cragg-Donald Wald F statistic):{res}{col 71}  10.053
{txt}                         (Kleibergen-Paap rk Wald F statistic):{res}{col 71}  10.045
{txt}Stock-Yogo weak ID test critical values:{res}{txt}{col 42}10% maximal IV size{res}{col 73} 16.38
{txt}{col 42}15% maximal IV size{res}{col 73}  8.96
{txt}{col 42}20% maximal IV size{res}{col 73}  6.66
{txt}{col 42}25% maximal IV size{res}{col 73}  5.53
{txt}Source: Stock-Yogo (2005).  Reproduced by permission.
NB: Critical values are for Cragg-Donald F statistic and i.i.d. errors.
{hline 78}
{help ivreg2##overidtests:Hansen J statistic} (overidentification test of all instruments):{res}{col 71}   0.000
{txt}{col 50}(equation exactly identified)
{hline 78}
Instrumented:{col 23}satis_head
Excluded instruments:{col 23}treatc
{hline 78}
({res}est5{txt} stored)

{com}. 
. estadd scalar fstat = e(widstat)

{txt}added scalar:
              e(fstat) =  {res}10.044707
{txt}
{com}. estadd local IV "SumIV"

{txt}added macro:
                 e(IV) : "{res:SumIV}"

{com}. estadd local nothing nothing2 = " "

{txt}added macro:
            e(nothing) : "{res:nothing2 = " "}"

{com}. get_mean

{txt}added scalar:
                 e(av) =  {res}.507
{txt}
{com}. 
. /* E.9. Col 6: 1 instrument + controls */       
. eststo: ivreg2 satis_dem ///
>         (satis_head = treatc) $controls $mv_controls i.country, small robust 
{txt}Warning - collinearities detected
Vars dropped:{col 21}mv_thirties mv_fourties mv_fifties mv_sixties mv_seventies
{col 21}mv_income2quartile mv_income3quartile mv_income4quartile
{col 21}mv_female mv_college mv_christiannotcatholic mv_catholic
{col 21}mv_fulltimeworker mv_unemployed mv_outofLF mv_black
{col 21}mv_latino mv_asian mv_bluecollar mv_serviceworker 4.country
{col 21}7.country 9.country 11.country 12.country
{res}
{txt}IV (2SLS) estimation
{hline 20}

Estimates efficient for homoskedasticity only
Statistics robust to heteroskedasticity

{col 55}Number of obs = {res}   20539
{txt}{col 55}F( 41, 20497) = {res}  107.56
{txt}{col 55}Prob > F      = {res}  0.0000
{txt}Total (centered) SS     = {res}  1444.70131{txt}{col 55}Centered R2   = {res}  0.4180
{txt}Total (uncentered) SS   = {res} 6722.940042{txt}{col 55}Uncentered R2 = {res}  0.8749
{txt}Residual SS             = {res} 840.7555867{txt}{col 55}Root MSE      = {res}   .2025

{txt}{hline 24}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 25}{c |}{col 37}    Robust
{col 1}              satis_dem{col 25}{c |} Coefficient{col 37}  std. err.{col 49}      t{col 57}   P>|t|{col 65}     [95% con{col 78}f. interval]
{hline 24}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 13}satis_head {c |}{col 25}{res}{space 2} .4075041{col 37}{space 2} .2018104{col 48}{space 1}    2.02{col 57}{space 3}0.043{col 65}{space 4} .0119395{col 78}{space 3} .8030686
{txt}{space 15}thirties {c |}{col 25}{res}{space 2}-.0200954{col 37}{space 2} .0074863{col 48}{space 1}   -2.68{col 57}{space 3}0.007{col 65}{space 4} -.034769{col 78}{space 3}-.0054217
{txt}{space 15}fourties {c |}{col 25}{res}{space 2}  -.01836{col 37}{space 2} .0106299{col 48}{space 1}   -1.73{col 57}{space 3}0.084{col 65}{space 4}-.0391954{col 78}{space 3} .0024754
{txt}{space 16}fifties {c |}{col 25}{res}{space 2}-.0140225{col 37}{space 2} .0102895{col 48}{space 1}   -1.36{col 57}{space 3}0.173{col 65}{space 4}-.0341907{col 78}{space 3} .0061457
{txt}{space 16}sixties {c |}{col 25}{res}{space 2}  .001243{col 37}{space 2} .0104022{col 48}{space 1}    0.12{col 57}{space 3}0.905{col 65}{space 4}-.0191462{col 78}{space 3} .0216322
{txt}{space 14}seventies {c |}{col 25}{res}{space 2} .0209621{col 37}{space 2} .0078548{col 48}{space 1}    2.67{col 57}{space 3}0.008{col 65}{space 4} .0055661{col 78}{space 3}  .036358
{txt}{space 8}income2quartile {c |}{col 25}{res}{space 2} .0206489{col 37}{space 2}  .005512{col 48}{space 1}    3.75{col 57}{space 3}0.000{col 65}{space 4}  .009845{col 78}{space 3} .0314529
{txt}{space 8}income3quartile {c |}{col 25}{res}{space 2} .0296015{col 37}{space 2} .0068752{col 48}{space 1}    4.31{col 57}{space 3}0.000{col 65}{space 4} .0161255{col 78}{space 3} .0430775
{txt}{space 8}income4quartile {c |}{col 25}{res}{space 2} .0472048{col 37}{space 2} .0102353{col 48}{space 1}    4.61{col 57}{space 3}0.000{col 65}{space 4} .0271428{col 78}{space 3} .0672669
{txt}{space 9}incomenoanswer {c |}{col 25}{res}{space 2} .0050589{col 37}{space 2} .0073006{col 48}{space 1}    0.69{col 57}{space 3}0.488{col 65}{space 4}-.0092509{col 78}{space 3} .0193687
{txt}{space 17}female {c |}{col 25}{res}{space 2}-.0057199{col 37}{space 2} .0039422{col 48}{space 1}   -1.45{col 57}{space 3}0.147{col 65}{space 4} -.013447{col 78}{space 3} .0020071
{txt}{space 13}highschool {c |}{col 25}{res}{space 2} .0047625{col 37}{space 2}  .004836{col 48}{space 1}    0.98{col 57}{space 3}0.325{col 65}{space 4}-.0047165{col 78}{space 3} .0142416
{txt}{space 16}college {c |}{col 25}{res}{space 2} .0276314{col 37}{space 2} .0045678{col 48}{space 1}    6.05{col 57}{space 3}0.000{col 65}{space 4} .0186781{col 78}{space 3} .0365848
{txt}{space 13}noreligion {c |}{col 25}{res}{space 2}-.0184422{col 37}{space 2} .0079601{col 48}{space 1}   -2.32{col 57}{space 3}0.021{col 65}{space 4}-.0340447{col 78}{space 3}-.0028397
{txt}{space 3}christiannotcatholic {c |}{col 25}{res}{space 2} .0162669{col 37}{space 2} .0141256{col 48}{space 1}    1.15{col 57}{space 3}0.250{col 65}{space 4}-.0114204{col 78}{space 3} .0439543
{txt}{space 15}catholic {c |}{col 25}{res}{space 2} .0127984{col 37}{space 2} .0081632{col 48}{space 1}    1.57{col 57}{space 3}0.117{col 65}{space 4}-.0032021{col 78}{space 3}  .028799
{txt}{space 9}fulltimeworker {c |}{col 25}{res}{space 2}-.0694158{col 37}{space 2} .0635937{col 48}{space 1}   -1.09{col 57}{space 3}0.275{col 65}{space 4}-.1940646{col 78}{space 3}  .055233
{txt}{space 9}parttimeworker {c |}{col 25}{res}{space 2}-.0428097{col 37}{space 2} .0644011{col 48}{space 1}   -0.66{col 57}{space 3}0.506{col 65}{space 4}-.1690409{col 78}{space 3} .0834215
{txt}{space 13}unemployed {c |}{col 25}{res}{space 2}-.0845171{col 37}{space 2} .0705113{col 48}{space 1}   -1.20{col 57}{space 3}0.231{col 65}{space 4}-.2227249{col 78}{space 3} .0536906
{txt}{space 11}selfemployed {c |}{col 25}{res}{space 2}-.0648658{col 37}{space 2} .0803104{col 48}{space 1}   -0.81{col 57}{space 3}0.419{col 65}{space 4}-.2222806{col 78}{space 3}  .092549
{txt}{space 16}outofLF {c |}{col 25}{res}{space 2} -.072609{col 37}{space 2} .0662084{col 48}{space 1}   -1.10{col 57}{space 3}0.273{col 65}{space 4}-.2023828{col 78}{space 3} .0571648
{txt}{space 13}goodhealth {c |}{col 25}{res}{space 2}  .031715{col 37}{space 2} .0104902{col 48}{space 1}    3.02{col 57}{space 3}0.003{col 65}{space 4} .0111534{col 78}{space 3} .0522765
{txt}{space 18}white {c |}{col 25}{res}{space 2}  .003102{col 37}{space 2} .0436871{col 48}{space 1}    0.07{col 57}{space 3}0.943{col 65}{space 4}-.0825283{col 78}{space 3} .0887322
{txt}{space 18}black {c |}{col 25}{res}{space 2} .0670905{col 37}{space 2} .0376537{col 48}{space 1}    1.78{col 57}{space 3}0.075{col 65}{space 4}-.0067137{col 78}{space 3} .1408947
{txt}{space 17}latino {c |}{col 25}{res}{space 2} .0590162{col 37}{space 2} .0401152{col 48}{space 1}    1.47{col 57}{space 3}0.141{col 65}{space 4}-.0196129{col 78}{space 3} .1376453
{txt}{space 18}asian {c |}{col 25}{res}{space 2} .0416422{col 37}{space 2}  .041394{col 48}{space 1}    1.01{col 57}{space 3}0.314{col 65}{space 4}-.0394934{col 78}{space 3} .1227778
{txt}{space 12}whitecollar {c |}{col 25}{res}{space 2} .0025269{col 37}{space 2} .0089967{col 48}{space 1}    0.28{col 57}{space 3}0.779{col 65}{space 4}-.0151073{col 78}{space 3} .0201611
{txt}{space 13}bluecollar {c |}{col 25}{res}{space 2}-.0227291{col 37}{space 2}  .013997{col 48}{space 1}   -1.62{col 57}{space 3}0.104{col 65}{space 4}-.0501644{col 78}{space 3} .0047062
{txt}{space 10}serviceworker {c |}{col 25}{res}{space 2}-.0087135{col 37}{space 2} .0078716{col 48}{space 1}   -1.11{col 57}{space 3}0.268{col 65}{space 4}-.0241424{col 78}{space 3} .0067154
{txt}{space 12}mv_thirties {c |}{col 25}{res}{space 2}        0{col 37}{txt}  (omitted)
{space 12}mv_fourties {c |}{col 25}{res}{space 2}        0{col 37}{txt}  (omitted)
{space 13}mv_fifties {c |}{col 25}{res}{space 2}        0{col 37}{txt}  (omitted)
{space 13}mv_sixties {c |}{col 25}{res}{space 2}        0{col 37}{txt}  (omitted)
{space 11}mv_seventies {c |}{col 25}{res}{space 2}        0{col 37}{txt}  (omitted)
{space 5}mv_income2quartile {c |}{col 25}{res}{space 2}        0{col 37}{txt}  (omitted)
{space 5}mv_income3quartile {c |}{col 25}{res}{space 2}        0{col 37}{txt}  (omitted)
{space 5}mv_income4quartile {c |}{col 25}{res}{space 2}        0{col 37}{txt}  (omitted)
{space 6}mv_incomenoanswer {c |}{col 25}{res}{space 2} .0205832{col 37}{space 2} .0170633{col 48}{space 1}    1.21{col 57}{space 3}0.228{col 65}{space 4}-.0128623{col 78}{space 3} .0540286
{txt}{space 14}mv_female {c |}{col 25}{res}{space 2}        0{col 37}{txt}  (omitted)
{space 10}mv_highschool {c |}{col 25}{res}{space 2}   .02516{col 37}{space 2} .0111168{col 48}{space 1}    2.26{col 57}{space 3}0.024{col 65}{space 4} .0033702{col 78}{space 3} .0469497
{txt}{space 13}mv_college {c |}{col 25}{res}{space 2}        0{col 37}{txt}  (omitted)
{space 10}mv_noreligion {c |}{col 25}{res}{space 2}-.0028452{col 37}{space 2} .0231157{col 48}{space 1}   -0.12{col 57}{space 3}0.902{col 65}{space 4}-.0481539{col 78}{space 3} .0424634
{txt}mv_christiannotcatholic {c |}{col 25}{res}{space 2}        0{col 37}{txt}  (omitted)
{space 12}mv_catholic {c |}{col 25}{res}{space 2}        0{col 37}{txt}  (omitted)
{space 6}mv_fulltimeworker {c |}{col 25}{res}{space 2}        0{col 37}{txt}  (omitted)
{space 6}mv_parttimeworker {c |}{col 25}{res}{space 2}-.0497852{col 37}{space 2} .0601215{col 48}{space 1}   -0.83{col 57}{space 3}0.408{col 65}{space 4}-.1676281{col 78}{space 3} .0680576
{txt}{space 10}mv_unemployed {c |}{col 25}{res}{space 2}        0{col 37}{txt}  (omitted)
{space 8}mv_selfemployed {c |}{col 25}{res}{space 2} .0644475{col 37}{space 2} .0617172{col 48}{space 1}    1.04{col 57}{space 3}0.296{col 65}{space 4}-.0565231{col 78}{space 3} .1854181
{txt}{space 13}mv_outofLF {c |}{col 25}{res}{space 2}        0{col 37}{txt}  (omitted)
{space 10}mv_goodhealth {c |}{col 25}{res}{space 2} .1002578{col 37}{space 2} .0801765{col 48}{space 1}    1.25{col 57}{space 3}0.211{col 65}{space 4}-.0568946{col 78}{space 3} .2574102
{txt}{space 15}mv_white {c |}{col 25}{res}{space 2} .0747133{col 37}{space 2} .0840253{col 48}{space 1}    0.89{col 57}{space 3}0.374{col 65}{space 4} -.089983{col 78}{space 3} .2394095
{txt}{space 15}mv_black {c |}{col 25}{res}{space 2}        0{col 37}{txt}  (omitted)
{space 14}mv_latino {c |}{col 25}{res}{space 2}        0{col 37}{txt}  (omitted)
{space 15}mv_asian {c |}{col 25}{res}{space 2}        0{col 37}{txt}  (omitted)
{space 9}mv_whitecollar {c |}{col 25}{res}{space 2} .0123254{col 37}{space 2} .0614832{col 48}{space 1}    0.20{col 57}{space 3}0.841{col 65}{space 4}-.1081866{col 78}{space 3} .1328373
{txt}{space 10}mv_bluecollar {c |}{col 25}{res}{space 2}        0{col 37}{txt}  (omitted)
{space 7}mv_serviceworker {c |}{col 25}{res}{space 2}        0{col 37}{txt}  (omitted)
{space 23} {c |}
{space 16}country {c |}
{space 15}Austria  {c |}{col 25}{res}{space 2} .1029347{col 37}{space 2} .1105846{col 48}{space 1}    0.93{col 57}{space 3}0.352{col 65}{space 4}-.1138199{col 78}{space 3} .3196894
{txt}{space 16}France  {c |}{col 25}{res}{space 2}        0{col 37}{txt}  (omitted)
{space 15}Germany  {c |}{col 25}{res}{space 2} .0862642{col 37}{space 2}  .118683{col 48}{space 1}    0.73{col 57}{space 3}0.467{col 65}{space 4} -.146364{col 78}{space 3} .3188924
{txt}{space 17}Italy  {c |}{col 25}{res}{space 2}-.0123803{col 37}{space 2} .1077302{col 48}{space 1}   -0.11{col 57}{space 3}0.909{col 65}{space 4}-.2235401{col 78}{space 3} .1987796
{txt}{space 11}New_Zealand  {c |}{col 25}{res}{space 2}        0{col 37}{txt}  (omitted)
{space 17}Spain  {c |}{col 25}{res}{space 2}        0{col 37}{txt}  (omitted)
{space 16}Sweden  {c |}{col 25}{res}{space 2} .0682492{col 37}{space 2} .0183743{col 48}{space 1}    3.71{col 57}{space 3}0.000{col 65}{space 4} .0322342{col 78}{space 3} .1042642
{txt}{space 20}UK  {c |}{col 25}{res}{space 2}        0{col 37}{txt}  (omitted)
{space 20}US  {c |}{col 25}{res}{space 2}        0{col 37}{txt}  (omitted)
{space 23} {c |}
{space 18}_cons {c |}{col 25}{res}{space 2} .2107241{col 37}{space 2} .0379716{col 48}{space 1}    5.55{col 57}{space 3}0.000{col 65}{space 4} .1362968{col 78}{space 3} .2851514
{txt}{hline 24}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{help ivreg2##idtest:Underidentification test} (Kleibergen-Paap rk LM statistic):{res}{col 71}  11.937
{txt}{col 52}Chi-sq({res}1{txt}) P-val =  {res}{col 73}0.0006
{txt}{hline 78}
{help ivreg2##widtest:Weak identification test} (Cragg-Donald Wald F statistic):{res}{col 71}  11.890
{txt}                         (Kleibergen-Paap rk Wald F statistic):{res}{col 71}  11.938
{txt}Stock-Yogo weak ID test critical values:{res}{txt}{col 42}10% maximal IV size{res}{col 73} 16.38
{txt}{col 42}15% maximal IV size{res}{col 73}  8.96
{txt}{col 42}20% maximal IV size{res}{col 73}  6.66
{txt}{col 42}25% maximal IV size{res}{col 73}  5.53
{txt}Source: Stock-Yogo (2005).  Reproduced by permission.
NB: Critical values are for Cragg-Donald F statistic and i.i.d. errors.
{hline 78}
{help ivreg2##overidtests:Hansen J statistic} (overidentification test of all instruments):{res}{col 71}   0.000
{txt}{col 50}(equation exactly identified)
{hline 78}
Instrumented:{col 23}satis_head
Included instruments:{col 23}thirties fourties fifties sixties seventies
{col 23}income2quartile income3quartile income4quartile
{col 23}incomenoanswer female highschool college noreligion
{col 23}christiannotcatholic catholic fulltimeworker
{col 23}parttimeworker unemployed selfemployed outofLF goodhealth
{col 23}white black latino asian whitecollar bluecollar
{col 23}serviceworker mv_incomenoanswer mv_highschool
{col 23}mv_noreligion mv_parttimeworker mv_selfemployed
{col 23}mv_goodhealth mv_white mv_whitecollar 2.country 5.country
{col 23}6.country 10.country
Excluded instruments:{col 23}treatc
Dropped collinear:{col 23}mv_thirties mv_fourties mv_fifties mv_sixties
{col 23}mv_seventies mv_income2quartile mv_income3quartile
{col 23}mv_income4quartile mv_female mv_college
{col 23}mv_christiannotcatholic mv_catholic mv_fulltimeworker
{col 23}mv_unemployed mv_outofLF mv_black mv_latino mv_asian
{col 23}mv_bluecollar mv_serviceworker 4.country 7.country
{col 23}9.country 11.country 12.country
{hline 78}
({res}est6{txt} stored)

{com}.         
. estadd scalar fstat = e(widstat)

{txt}added scalar:
              e(fstat) =  {res}11.937889
{txt}
{com}. estadd local IV "SumIV"

{txt}added macro:
                 e(IV) : "{res:SumIV}"

{com}. estadd local nothing nothing2 = " "

{txt}added macro:
            e(nothing) : "{res:nothing2 = " "}"

{com}. estadd local Controls "X", replace

{txt}added macro:
           e(Controls) : "{res:X}"

{com}. estadd local CFE "X", replace

{txt}added macro:
                e(CFE) : "{res:X}"

{com}. get_mean

{txt}added scalar:
                 e(av) =  {res}.507
{txt}
{com}. 
. esttab using "3_output/2_OA/Tables/TableE9.tex", depvar keep(satis_head) ///
>          label replace wrap ///
>         cells(b(star fmt(%15.3fc)) se(par fmt(%15.3fc))) interaction(" $\times$ ") ///
>         star(* 0.10 ** 0.05 *** 0.01) ///
>         stats(Controls CFE N av IV fstat , layout(@ @ @ @ @ @ ) fmt(%15s %15s %15.0fc %9.3f %9.3f %9.3f %9.3f ) ///
>         labels("Individual controls" "Country FE" Observations "Outcome mean" "Instruments" "F-statistic" )) ///
>         collabels(none) mlabels(none) ///
>         mgroups("Satisfaction with democracy" , pattern(1 0 0 0) ///
>         prefix(\multicolumn{c -(}@span{c )-}{c -(}c{c )-}{c -(}) suffix({c )-}) span erepeat(\cmidrule(lr){c -(}@span{c )-}))
{res}{txt}(output written to {browse  `"3_output/2_OA/Tables/TableE9.tex"'})

{com}.                 
. 
. **# Table E.10: Impact on support for democratic ideals, excluding Brazil and Poland - 2SLS.
. /* E.10. Col 1 */
. eststo clear
{txt}
{com}. eststo: ivreg2 strong_leader ///
>         (satis_head = TH1_TE1 TH1_TE2 TH1_TE3 TH1_TE4 TH2_TE1 TH2_TE2 TH2_TE3 TH2_TE4 TH3_TE1 TH3_TE2 TH3_TE3 TH3_TE4 TH4_TE1 TH4_TE2 TH4_TE3) $controls $mv_controls i.country, small robust 
{txt}Warning - collinearities detected
Vars dropped:{col 21}mv_thirties mv_fourties mv_fifties mv_sixties mv_seventies
{col 21}mv_income2quartile mv_income3quartile mv_income4quartile
{col 21}mv_female mv_college mv_christiannotcatholic mv_catholic
{col 21}mv_fulltimeworker mv_unemployed mv_outofLF mv_black
{col 21}mv_latino mv_asian mv_bluecollar mv_serviceworker 4.country
{col 21}7.country 9.country 11.country 12.country
{res}
{txt}IV (2SLS) estimation
{hline 20}

Estimates efficient for homoskedasticity only
Statistics robust to heteroskedasticity

{col 55}Number of obs = {res}   20534
{txt}{col 55}F( 41, 20492) = {res}   36.80
{txt}{col 55}Prob > F      = {res}  0.0000
{txt}Total (centered) SS     = {res} 4352.355313{txt}{col 55}Centered R2   = {res}  0.0620
{txt}Total (uncentered) SS   = {res}        6262{txt}{col 55}Uncentered R2 = {res}  0.3480
{txt}Residual SS             = {res} 4082.694622{txt}{col 55}Root MSE      = {res}   .4464

{txt}{hline 24}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 25}{c |}{col 37}    Robust
{col 1}          strong_leader{col 25}{c |} Coefficient{col 37}  std. err.{col 49}      t{col 57}   P>|t|{col 65}     [95% con{col 78}f. interval]
{hline 24}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 13}satis_head {c |}{col 25}{res}{space 2}-.0166415{col 37}{space 2} .2895096{col 48}{space 1}   -0.06{col 57}{space 3}0.954{col 65}{space 4}-.5841034{col 78}{space 3} .5508205
{txt}{space 15}thirties {c |}{col 25}{res}{space 2} .0298546{col 37}{space 2} .0142967{col 48}{space 1}    2.09{col 57}{space 3}0.037{col 65}{space 4}  .001832{col 78}{space 3} .0578773
{txt}{space 15}fourties {c |}{col 25}{res}{space 2}-.0118622{col 37}{space 2} .0178638{col 48}{space 1}   -0.66{col 57}{space 3}0.507{col 65}{space 4}-.0468766{col 78}{space 3} .0231522
{txt}{space 16}fifties {c |}{col 25}{res}{space 2} -.056211{col 37}{space 2} .0173417{col 48}{space 1}   -3.24{col 57}{space 3}0.001{col 65}{space 4}-.0902021{col 78}{space 3}-.0222198
{txt}{space 16}sixties {c |}{col 25}{res}{space 2} -.079603{col 37}{space 2} .0173946{col 48}{space 1}   -4.58{col 57}{space 3}0.000{col 65}{space 4}-.1136978{col 78}{space 3}-.0455082
{txt}{space 14}seventies {c |}{col 25}{res}{space 2}-.0816905{col 37}{space 2} .0150072{col 48}{space 1}   -5.44{col 57}{space 3}0.000{col 65}{space 4}-.1111059{col 78}{space 3}-.0522752
{txt}{space 8}income2quartile {c |}{col 25}{res}{space 2} -.016226{col 37}{space 2} .0106447{col 48}{space 1}   -1.52{col 57}{space 3}0.127{col 65}{space 4}-.0370904{col 78}{space 3} .0046384
{txt}{space 8}income3quartile {c |}{col 25}{res}{space 2}-.0161898{col 37}{space 2} .0122512{col 48}{space 1}   -1.32{col 57}{space 3}0.186{col 65}{space 4}-.0402031{col 78}{space 3} .0078234
{txt}{space 8}income4quartile {c |}{col 25}{res}{space 2} -.041046{col 37}{space 2} .0164768{col 48}{space 1}   -2.49{col 57}{space 3}0.013{col 65}{space 4}-.0733418{col 78}{space 3}-.0087502
{txt}{space 9}incomenoanswer {c |}{col 25}{res}{space 2}-.0066925{col 37}{space 2} .0147879{col 48}{space 1}   -0.45{col 57}{space 3}0.651{col 65}{space 4}-.0356779{col 78}{space 3} .0222929
{txt}{space 17}female {c |}{col 25}{res}{space 2}-.0231721{col 37}{space 2} .0074151{col 48}{space 1}   -3.12{col 57}{space 3}0.002{col 65}{space 4}-.0377064{col 78}{space 3}-.0086379
{txt}{space 13}highschool {c |}{col 25}{res}{space 2} -.103733{col 37}{space 2} .0112241{col 48}{space 1}   -9.24{col 57}{space 3}0.000{col 65}{space 4}-.1257332{col 78}{space 3}-.0817329
{txt}{space 16}college {c |}{col 25}{res}{space 2}-.1625744{col 37}{space 2} .0104963{col 48}{space 1}  -15.49{col 57}{space 3}0.000{col 65}{space 4} -.183148{col 78}{space 3}-.1420009
{txt}{space 13}noreligion {c |}{col 25}{res}{space 2}-.1396572{col 37}{space 2} .0157249{col 48}{space 1}   -8.88{col 57}{space 3}0.000{col 65}{space 4}-.1704794{col 78}{space 3}-.1088351
{txt}{space 3}christiannotcatholic {c |}{col 25}{res}{space 2}-.0771062{col 37}{space 2} .0236523{col 48}{space 1}   -3.26{col 57}{space 3}0.001{col 65}{space 4}-.1234665{col 78}{space 3}-.0307458
{txt}{space 15}catholic {c |}{col 25}{res}{space 2}-.0303677{col 37}{space 2} .0164308{col 48}{space 1}   -1.85{col 57}{space 3}0.065{col 65}{space 4}-.0625735{col 78}{space 3}  .001838
{txt}{space 9}fulltimeworker {c |}{col 25}{res}{space 2}-.0559257{col 37}{space 2}  .092387{col 48}{space 1}   -0.61{col 57}{space 3}0.545{col 65}{space 4}-.2370117{col 78}{space 3} .1251602
{txt}{space 9}parttimeworker {c |}{col 25}{res}{space 2}-.1056342{col 37}{space 2} .0965693{col 48}{space 1}   -1.09{col 57}{space 3}0.274{col 65}{space 4}-.2949177{col 78}{space 3} .0836493
{txt}{space 13}unemployed {c |}{col 25}{res}{space 2}-.0894822{col 37}{space 2} .1034755{col 48}{space 1}   -0.86{col 57}{space 3}0.387{col 65}{space 4}-.2923024{col 78}{space 3} .1133379
{txt}{space 11}selfemployed {c |}{col 25}{res}{space 2} -.024128{col 37}{space 2} .1205595{col 48}{space 1}   -0.20{col 57}{space 3}0.841{col 65}{space 4}-.2604342{col 78}{space 3} .2121782
{txt}{space 16}outofLF {c |}{col 25}{res}{space 2}-.0418917{col 37}{space 2}    .0963{col 48}{space 1}   -0.44{col 57}{space 3}0.664{col 65}{space 4}-.2306474{col 78}{space 3}  .146864
{txt}{space 13}goodhealth {c |}{col 25}{res}{space 2} .0022022{col 37}{space 2} .0159448{col 48}{space 1}    0.14{col 57}{space 3}0.890{col 65}{space 4}-.0290508{col 78}{space 3} .0334552
{txt}{space 18}white {c |}{col 25}{res}{space 2} .0485267{col 37}{space 2} .0733371{col 48}{space 1}    0.66{col 57}{space 3}0.508{col 65}{space 4}-.0952198{col 78}{space 3} .1922732
{txt}{space 18}black {c |}{col 25}{res}{space 2}  .143483{col 37}{space 2} .0704028{col 48}{space 1}    2.04{col 57}{space 3}0.042{col 65}{space 4} .0054879{col 78}{space 3} .2814782
{txt}{space 17}latino {c |}{col 25}{res}{space 2} .1753378{col 37}{space 2} .0774851{col 48}{space 1}    2.26{col 57}{space 3}0.024{col 65}{space 4} .0234609{col 78}{space 3} .3272147
{txt}{space 18}asian {c |}{col 25}{res}{space 2} .1431702{col 37}{space 2} .0794306{col 48}{space 1}    1.80{col 57}{space 3}0.071{col 65}{space 4}-.0125201{col 78}{space 3} .2988606
{txt}{space 12}whitecollar {c |}{col 25}{res}{space 2} .0088722{col 37}{space 2}  .016811{col 48}{space 1}    0.53{col 57}{space 3}0.598{col 65}{space 4}-.0240788{col 78}{space 3} .0418232
{txt}{space 13}bluecollar {c |}{col 25}{res}{space 2} .0466226{col 37}{space 2}   .02391{col 48}{space 1}    1.95{col 57}{space 3}0.051{col 65}{space 4}-.0002429{col 78}{space 3} .0934881
{txt}{space 10}serviceworker {c |}{col 25}{res}{space 2} .0129672{col 37}{space 2} .0147929{col 48}{space 1}    0.88{col 57}{space 3}0.381{col 65}{space 4} -.016028{col 78}{space 3} .0419624
{txt}{space 12}mv_thirties {c |}{col 25}{res}{space 2}        0{col 37}{txt}  (omitted)
{space 12}mv_fourties {c |}{col 25}{res}{space 2}        0{col 37}{txt}  (omitted)
{space 13}mv_fifties {c |}{col 25}{res}{space 2}        0{col 37}{txt}  (omitted)
{space 13}mv_sixties {c |}{col 25}{res}{space 2}        0{col 37}{txt}  (omitted)
{space 11}mv_seventies {c |}{col 25}{res}{space 2}        0{col 37}{txt}  (omitted)
{space 5}mv_income2quartile {c |}{col 25}{res}{space 2}        0{col 37}{txt}  (omitted)
{space 5}mv_income3quartile {c |}{col 25}{res}{space 2}        0{col 37}{txt}  (omitted)
{space 5}mv_income4quartile {c |}{col 25}{res}{space 2}        0{col 37}{txt}  (omitted)
{space 6}mv_incomenoanswer {c |}{col 25}{res}{space 2} .0935532{col 37}{space 2} .0306261{col 48}{space 1}    3.05{col 57}{space 3}0.002{col 65}{space 4} .0335237{col 78}{space 3} .1535827
{txt}{space 14}mv_female {c |}{col 25}{res}{space 2}        0{col 37}{txt}  (omitted)
{space 10}mv_highschool {c |}{col 25}{res}{space 2}-.0602694{col 37}{space 2} .0268202{col 48}{space 1}   -2.25{col 57}{space 3}0.025{col 65}{space 4}-.1128391{col 78}{space 3}-.0076997
{txt}{space 13}mv_college {c |}{col 25}{res}{space 2}        0{col 37}{txt}  (omitted)
{space 10}mv_noreligion {c |}{col 25}{res}{space 2}-.0280681{col 37}{space 2} .0465907{col 48}{space 1}   -0.60{col 57}{space 3}0.547{col 65}{space 4}-.1193896{col 78}{space 3} .0632534
{txt}mv_christiannotcatholic {c |}{col 25}{res}{space 2}        0{col 37}{txt}  (omitted)
{space 12}mv_catholic {c |}{col 25}{res}{space 2}        0{col 37}{txt}  (omitted)
{space 6}mv_fulltimeworker {c |}{col 25}{res}{space 2}        0{col 37}{txt}  (omitted)
{space 6}mv_parttimeworker {c |}{col 25}{res}{space 2}-.0523822{col 37}{space 2} .0893142{col 48}{space 1}   -0.59{col 57}{space 3}0.558{col 65}{space 4}-.2274451{col 78}{space 3} .1226808
{txt}{space 10}mv_unemployed {c |}{col 25}{res}{space 2}        0{col 37}{txt}  (omitted)
{space 8}mv_selfemployed {c |}{col 25}{res}{space 2} .0819623{col 37}{space 2} .0917433{col 48}{space 1}    0.89{col 57}{space 3}0.372{col 65}{space 4} -.097862{col 78}{space 3} .2617865
{txt}{space 13}mv_outofLF {c |}{col 25}{res}{space 2}        0{col 37}{txt}  (omitted)
{space 10}mv_goodhealth {c |}{col 25}{res}{space 2} .1765485{col 37}{space 2} .1679291{col 48}{space 1}    1.05{col 57}{space 3}0.293{col 65}{space 4}-.1526058{col 78}{space 3} .5057029
{txt}{space 15}mv_white {c |}{col 25}{res}{space 2} .1364824{col 37}{space 2} .1266515{col 48}{space 1}    1.08{col 57}{space 3}0.281{col 65}{space 4}-.1117645{col 78}{space 3} .3847294
{txt}{space 15}mv_black {c |}{col 25}{res}{space 2}        0{col 37}{txt}  (omitted)
{space 14}mv_latino {c |}{col 25}{res}{space 2}        0{col 37}{txt}  (omitted)
{space 15}mv_asian {c |}{col 25}{res}{space 2}        0{col 37}{txt}  (omitted)
{space 9}mv_whitecollar {c |}{col 25}{res}{space 2} .1660569{col 37}{space 2} .0922309{col 48}{space 1}    1.80{col 57}{space 3}0.072{col 65}{space 4} -.014723{col 78}{space 3} .3468368
{txt}{space 10}mv_bluecollar {c |}{col 25}{res}{space 2}        0{col 37}{txt}  (omitted)
{space 7}mv_serviceworker {c |}{col 25}{res}{space 2}        0{col 37}{txt}  (omitted)
{space 23} {c |}
{space 16}country {c |}
{space 15}Austria  {c |}{col 25}{res}{space 2} .0604988{col 37}{space 2} .1645392{col 48}{space 1}    0.37{col 57}{space 3}0.713{col 65}{space 4}-.2620111{col 78}{space 3} .3830087
{txt}{space 16}France  {c |}{col 25}{res}{space 2}        0{col 37}{txt}  (omitted)
{space 15}Germany  {c |}{col 25}{res}{space 2} .1482422{col 37}{space 2} .1759349{col 48}{space 1}    0.84{col 57}{space 3}0.399{col 65}{space 4}-.1966043{col 78}{space 3} .4930887
{txt}{space 17}Italy  {c |}{col 25}{res}{space 2} .2963428{col 37}{space 2}  .164543{col 48}{space 1}    1.80{col 57}{space 3}0.072{col 65}{space 4}-.0261746{col 78}{space 3} .6188602
{txt}{space 11}New_Zealand  {c |}{col 25}{res}{space 2}        0{col 37}{txt}  (omitted)
{space 17}Spain  {c |}{col 25}{res}{space 2}        0{col 37}{txt}  (omitted)
{space 16}Sweden  {c |}{col 25}{res}{space 2} .0362924{col 37}{space 2} .0354234{col 48}{space 1}    1.02{col 57}{space 3}0.306{col 65}{space 4}-.0331402{col 78}{space 3} .1057251
{txt}{space 20}UK  {c |}{col 25}{res}{space 2}        0{col 37}{txt}  (omitted)
{space 20}US  {c |}{col 25}{res}{space 2}        0{col 37}{txt}  (omitted)
{space 23} {c |}
{space 18}_cons {c |}{col 25}{res}{space 2} .2919519{col 37}{space 2} .0732657{col 48}{space 1}    3.98{col 57}{space 3}0.000{col 65}{space 4} .1483454{col 78}{space 3} .4355585
{txt}{hline 24}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{help ivreg2##idtest:Underidentification test} (Kleibergen-Paap rk LM statistic):{res}{col 71}  28.393
{txt}{col 52}Chi-sq({res}15{txt}) P-val =  {res}{col 73}0.0192
{txt}{hline 78}
{help ivreg2##widtest:Weak identification test} (Cragg-Donald Wald F statistic):{res}{col 71}   1.856
{txt}                         (Kleibergen-Paap rk Wald F statistic):{res}{col 71}   1.897
{txt}Stock-Yogo weak ID test critical values:{res}{txt}{col 42} 5% maximal IV relative bias{res}{col 73} 21.23
{txt}{col 42}10% maximal IV relative bias{res}{col 73} 11.51
{txt}{col 42}20% maximal IV relative bias{res}{col 73}  6.42
{txt}{col 42}30% maximal IV relative bias{res}{col 73}  4.63
{txt}{col 42}10% maximal IV size{res}{col 73} 50.39
{txt}{col 42}15% maximal IV size{res}{col 73} 26.80
{txt}{col 42}20% maximal IV size{res}{col 73} 18.72
{txt}{col 42}25% maximal IV size{res}{col 73} 14.60
{txt}Source: Stock-Yogo (2005).  Reproduced by permission.
NB: Critical values are for Cragg-Donald F statistic and i.i.d. errors.
{hline 78}
{help ivreg2##overidtests:Hansen J statistic} (overidentification test of all instruments):{res}{col 71}  19.086
{txt}{col 52}Chi-sq({res}14{txt}) P-val =  {res}{col 73}0.1617
{txt}{hline 78}
Instrumented:{col 23}satis_head
Included instruments:{col 23}thirties fourties fifties sixties seventies
{col 23}income2quartile income3quartile income4quartile
{col 23}incomenoanswer female highschool college noreligion
{col 23}christiannotcatholic catholic fulltimeworker
{col 23}parttimeworker unemployed selfemployed outofLF goodhealth
{col 23}white black latino asian whitecollar bluecollar
{col 23}serviceworker mv_incomenoanswer mv_highschool
{col 23}mv_noreligion mv_parttimeworker mv_selfemployed
{col 23}mv_goodhealth mv_white mv_whitecollar 2.country 5.country
{col 23}6.country 10.country
Excluded instruments:{col 23}TH1_TE1 TH1_TE2 TH1_TE3 TH1_TE4 TH2_TE1 TH2_TE2 TH2_TE3
{col 23}TH2_TE4 TH3_TE1 TH3_TE2 TH3_TE3 TH3_TE4 TH4_TE1 TH4_TE2
{col 23}TH4_TE3
Dropped collinear:{col 23}mv_thirties mv_fourties mv_fifties mv_sixties
{col 23}mv_seventies mv_income2quartile mv_income3quartile
{col 23}mv_income4quartile mv_female mv_college
{col 23}mv_christiannotcatholic mv_catholic mv_fulltimeworker
{col 23}mv_unemployed mv_outofLF mv_black mv_latino mv_asian
{col 23}mv_bluecollar mv_serviceworker 4.country 7.country
{col 23}9.country 11.country 12.country
{hline 78}
({res}est1{txt} stored)

{com}. 
. estadd scalar fstat = e(widstat)

{txt}added scalar:
              e(fstat) =  {res}1.8974057
{txt}
{com}. estadd local CFE "X", replace

{txt}added macro:
                e(CFE) : "{res:X}"

{com}. estadd local Controls "X", replace

{txt}added macro:
           e(Controls) : "{res:X}"

{com}. estadd local IV "16 IVs", replace

{txt}added macro:
                 e(IV) : "{res:16 IVs}"

{com}. get_mean

{txt}added scalar:
                 e(av) =  {res}.305
{txt}
{com}. 
. /* E.10. Col 2 */
. eststo: ivreg2 strong_leader ///
>         (satis_head = treatc) $controls $mv_controls i.country, small robust 
{txt}Warning - collinearities detected
Vars dropped:{col 21}mv_thirties mv_fourties mv_fifties mv_sixties mv_seventies
{col 21}mv_income2quartile mv_income3quartile mv_income4quartile
{col 21}mv_female mv_college mv_christiannotcatholic mv_catholic
{col 21}mv_fulltimeworker mv_unemployed mv_outofLF mv_black
{col 21}mv_latino mv_asian mv_bluecollar mv_serviceworker 4.country
{col 21}7.country 9.country 11.country 12.country
{res}
{txt}IV (2SLS) estimation
{hline 20}

Estimates efficient for homoskedasticity only
Statistics robust to heteroskedasticity

{col 55}Number of obs = {res}   20534
{txt}{col 55}F( 41, 20492) = {res}   36.97
{txt}{col 55}Prob > F      = {res}  0.0000
{txt}Total (centered) SS     = {res} 4352.355313{txt}{col 55}Centered R2   = {res}  0.0651
{txt}Total (uncentered) SS   = {res}        6262{txt}{col 55}Uncentered R2 = {res}  0.3502
{txt}Residual SS             = {res} 4068.837638{txt}{col 55}Root MSE      = {res}   .4456

{txt}{hline 24}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 25}{c |}{col 37}    Robust
{col 1}          strong_leader{col 25}{c |} Coefficient{col 37}  std. err.{col 49}      t{col 57}   P>|t|{col 65}     [95% con{col 78}f. interval]
{hline 24}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 13}satis_head {c |}{col 25}{res}{space 2} .1638707{col 37}{space 2} .4480229{col 48}{space 1}    0.37{col 57}{space 3}0.715{col 65}{space 4}  -.71429{col 78}{space 3} 1.042031
{txt}{space 15}thirties {c |}{col 25}{res}{space 2} .0349031{col 37}{space 2} .0171621{col 48}{space 1}    2.03{col 57}{space 3}0.042{col 65}{space 4}  .001264{col 78}{space 3} .0685423
{txt}{space 15}fourties {c |}{col 25}{res}{space 2}-.0033138{col 37}{space 2} .0241273{col 48}{space 1}   -0.14{col 57}{space 3}0.891{col 65}{space 4}-.0506054{col 78}{space 3} .0439777
{txt}{space 16}fifties {c |}{col 25}{res}{space 2}-.0482736{col 37}{space 2}  .022932{col 48}{space 1}   -2.11{col 57}{space 3}0.035{col 65}{space 4}-.0932222{col 78}{space 3}-.0033249
{txt}{space 16}sixties {c |}{col 25}{res}{space 2}-.0715459{col 37}{space 2} .0231674{col 48}{space 1}   -3.09{col 57}{space 3}0.002{col 65}{space 4}-.1169559{col 78}{space 3}-.0261359
{txt}{space 14}seventies {c |}{col 25}{res}{space 2}-.0773712{col 37}{space 2} .0171179{col 48}{space 1}   -4.52{col 57}{space 3}0.000{col 65}{space 4}-.1109237{col 78}{space 3}-.0438186
{txt}{space 8}income2quartile {c |}{col 25}{res}{space 2} -.019388{col 37}{space 2} .0122171{col 48}{space 1}   -1.59{col 57}{space 3}0.113{col 65}{space 4}-.0433345{col 78}{space 3} .0045586
{txt}{space 8}income3quartile {c |}{col 25}{res}{space 2}-.0209136{col 37}{space 2}  .015152{col 48}{space 1}   -1.38{col 57}{space 3}0.168{col 65}{space 4}-.0506127{col 78}{space 3} .0087854
{txt}{space 8}income4quartile {c |}{col 25}{res}{space 2}-.0492946{col 37}{space 2} .0226544{col 48}{space 1}   -2.18{col 57}{space 3}0.030{col 65}{space 4} -.093699{col 78}{space 3}-.0048903
{txt}{space 9}incomenoanswer {c |}{col 25}{res}{space 2}-.0050926{col 37}{space 2} .0151722{col 48}{space 1}   -0.34{col 57}{space 3}0.737{col 65}{space 4}-.0348313{col 78}{space 3} .0246461
{txt}{space 17}female {c |}{col 25}{res}{space 2}-.0255616{col 37}{space 2} .0086632{col 48}{space 1}   -2.95{col 57}{space 3}0.003{col 65}{space 4}-.0425422{col 78}{space 3}-.0085811
{txt}{space 13}highschool {c |}{col 25}{res}{space 2}-.1046527{col 37}{space 2} .0113427{col 48}{space 1}   -9.23{col 57}{space 3}0.000{col 65}{space 4}-.1268853{col 78}{space 3}-.0824202
{txt}{space 16}college {c |}{col 25}{res}{space 2}-.1636276{col 37}{space 2} .0106687{col 48}{space 1}  -15.34{col 57}{space 3}0.000{col 65}{space 4}-.1845392{col 78}{space 3}-.1427161
{txt}{space 13}noreligion {c |}{col 25}{res}{space 2}-.1354584{col 37}{space 2} .0174059{col 48}{space 1}   -7.78{col 57}{space 3}0.000{col 65}{space 4}-.1695753{col 78}{space 3}-.1013415
{txt}{space 3}christiannotcatholic {c |}{col 25}{res}{space 2}-.0879015{col 37}{space 2} .0311681{col 48}{space 1}   -2.82{col 57}{space 3}0.005{col 65}{space 4}-.1489934{col 78}{space 3}-.0268096
{txt}{space 15}catholic {c |}{col 25}{res}{space 2}-.0348478{col 37}{space 2} .0183376{col 48}{space 1}   -1.90{col 57}{space 3}0.057{col 65}{space 4}-.0707909{col 78}{space 3} .0010953
{txt}{space 9}fulltimeworker {c |}{col 25}{res}{space 2} .0007891{col 37}{space 2} .1415303{col 48}{space 1}    0.01{col 57}{space 3}0.996{col 65}{space 4}-.2766216{col 78}{space 3} .2781999
{txt}{space 9}parttimeworker {c |}{col 25}{res}{space 2} -.050052{col 37}{space 2} .1430351{col 48}{space 1}   -0.35{col 57}{space 3}0.726{col 65}{space 4}-.3304123{col 78}{space 3} .2303083
{txt}{space 13}unemployed {c |}{col 25}{res}{space 2}-.0269234{col 37}{space 2} .1569613{col 48}{space 1}   -0.17{col 57}{space 3}0.864{col 65}{space 4}  -.33458{col 78}{space 3} .2807333
{txt}{space 11}selfemployed {c |}{col 25}{res}{space 2}  .042637{col 37}{space 2}  .175011{col 48}{space 1}    0.24{col 57}{space 3}0.808{col 65}{space 4}-.3003986{col 78}{space 3} .3856725
{txt}{space 16}outofLF {c |}{col 25}{res}{space 2} .0172353{col 37}{space 2} .1476275{col 48}{space 1}    0.12{col 57}{space 3}0.907{col 65}{space 4}-.2721263{col 78}{space 3} .3065968
{txt}{space 13}goodhealth {c |}{col 25}{res}{space 2}-.0067982{col 37}{space 2} .0233907{col 48}{space 1}   -0.29{col 57}{space 3}0.771{col 65}{space 4}-.0526458{col 78}{space 3} .0390494
{txt}{space 18}white {c |}{col 25}{res}{space 2} .0245771{col 37}{space 2} .0851598{col 48}{space 1}    0.29{col 57}{space 3}0.773{col 65}{space 4} -.142343{col 78}{space 3} .1914971
{txt}{space 18}black {c |}{col 25}{res}{space 2} .1410949{col 37}{space 2} .0693857{col 48}{space 1}    2.03{col 57}{space 3}0.042{col 65}{space 4} .0050934{col 78}{space 3} .2770964
{txt}{space 17}latino {c |}{col 25}{res}{space 2} .1692069{col 37}{space 2}  .076822{col 48}{space 1}    2.20{col 57}{space 3}0.028{col 65}{space 4} .0186297{col 78}{space 3} .3197841
{txt}{space 18}asian {c |}{col 25}{res}{space 2} .1304346{col 37}{space 2} .0813686{col 48}{space 1}    1.60{col 57}{space 3}0.109{col 65}{space 4}-.0290545{col 78}{space 3} .2899236
{txt}{space 12}whitecollar {c |}{col 25}{res}{space 2} .0124062{col 37}{space 2} .0180493{col 48}{space 1}    0.69{col 57}{space 3}0.492{col 65}{space 4}-.0229719{col 78}{space 3} .0477843
{txt}{space 13}bluecollar {c |}{col 25}{res}{space 2} .0565418{col 37}{space 2} .0305248{col 48}{space 1}    1.85{col 57}{space 3}0.064{col 65}{space 4}-.0032893{col 78}{space 3}  .116373
{txt}{space 10}serviceworker {c |}{col 25}{res}{space 2} .0170051{col 37}{space 2} .0166953{col 48}{space 1}    1.02{col 57}{space 3}0.308{col 65}{space 4}-.0157191{col 78}{space 3} .0497292
{txt}{space 12}mv_thirties {c |}{col 25}{res}{space 2}        0{col 37}{txt}  (omitted)
{space 12}mv_fourties {c |}{col 25}{res}{space 2}        0{col 37}{txt}  (omitted)
{space 13}mv_fifties {c |}{col 25}{res}{space 2}        0{col 37}{txt}  (omitted)
{space 13}mv_sixties {c |}{col 25}{res}{space 2}        0{col 37}{txt}  (omitted)
{space 11}mv_seventies {c |}{col 25}{res}{space 2}        0{col 37}{txt}  (omitted)
{space 5}mv_income2quartile {c |}{col 25}{res}{space 2}        0{col 37}{txt}  (omitted)
{space 5}mv_income3quartile {c |}{col 25}{res}{space 2}        0{col 37}{txt}  (omitted)
{space 5}mv_income4quartile {c |}{col 25}{res}{space 2}        0{col 37}{txt}  (omitted)
{space 6}mv_incomenoanswer {c |}{col 25}{res}{space 2} .1069113{col 37}{space 2} .0395244{col 48}{space 1}    2.70{col 57}{space 3}0.007{col 65}{space 4} .0294403{col 78}{space 3} .1843823
{txt}{space 14}mv_female {c |}{col 25}{res}{space 2}        0{col 37}{txt}  (omitted)
{space 10}mv_highschool {c |}{col 25}{res}{space 2}-.0585036{col 37}{space 2} .0270708{col 48}{space 1}   -2.16{col 57}{space 3}0.031{col 65}{space 4}-.1115645{col 78}{space 3}-.0054428
{txt}{space 13}mv_college {c |}{col 25}{res}{space 2}        0{col 37}{txt}  (omitted)
{space 10}mv_noreligion {c |}{col 25}{res}{space 2}-.0299257{col 37}{space 2} .0468651{col 48}{space 1}   -0.64{col 57}{space 3}0.523{col 65}{space 4} -.121785{col 78}{space 3} .0619337
{txt}mv_christiannotcatholic {c |}{col 25}{res}{space 2}        0{col 37}{txt}  (omitted)
{space 12}mv_catholic {c |}{col 25}{res}{space 2}        0{col 37}{txt}  (omitted)
{space 6}mv_fulltimeworker {c |}{col 25}{res}{space 2}        0{col 37}{txt}  (omitted)
{space 6}mv_parttimeworker {c |}{col 25}{res}{space 2} .0000814{col 37}{space 2} .1332626{col 48}{space 1}    0.00{col 57}{space 3}1.000{col 65}{space 4} -.261124{col 78}{space 3} .2612867
{txt}{space 10}mv_unemployed {c |}{col 25}{res}{space 2}        0{col 37}{txt}  (omitted)
{space 8}mv_selfemployed {c |}{col 25}{res}{space 2}  .028191{col 37}{space 2} .1366821{col 48}{space 1}    0.21{col 57}{space 3}0.837{col 65}{space 4}-.2397168{col 78}{space 3} .2960987
{txt}{space 13}mv_outofLF {c |}{col 25}{res}{space 2}        0{col 37}{txt}  (omitted)
{space 10}mv_goodhealth {c |}{col 25}{res}{space 2} .1426893{col 37}{space 2} .1832915{col 48}{space 1}    0.78{col 57}{space 3}0.436{col 65}{space 4}-.2165766{col 78}{space 3} .5019553
{txt}{space 15}mv_white {c |}{col 25}{res}{space 2} .0679262{col 37}{space 2} .1806099{col 48}{space 1}    0.38{col 57}{space 3}0.707{col 65}{space 4}-.2860836{col 78}{space 3} .4219361
{txt}{space 15}mv_black {c |}{col 25}{res}{space 2}        0{col 37}{txt}  (omitted)
{space 14}mv_latino {c |}{col 25}{res}{space 2}        0{col 37}{txt}  (omitted)
{space 15}mv_asian {c |}{col 25}{res}{space 2}        0{col 37}{txt}  (omitted)
{space 9}mv_whitecollar {c |}{col 25}{res}{space 2} .1126364{col 37}{space 2} .1366982{col 48}{space 1}    0.82{col 57}{space 3}0.410{col 65}{space 4}-.1553029{col 78}{space 3} .3805758
{txt}{space 10}mv_bluecollar {c |}{col 25}{res}{space 2}        0{col 37}{txt}  (omitted)
{space 7}mv_serviceworker {c |}{col 25}{res}{space 2}        0{col 37}{txt}  (omitted)
{space 23} {c |}
{space 16}country {c |}
{space 15}Austria  {c |}{col 25}{res}{space 2}-.0322634{col 37}{space 2}  .239877{col 48}{space 1}   -0.13{col 57}{space 3}0.893{col 65}{space 4}-.5024414{col 78}{space 3} .4379146
{txt}{space 16}France  {c |}{col 25}{res}{space 2}        0{col 37}{txt}  (omitted)
{space 15}Germany  {c |}{col 25}{res}{space 2} .0477469{col 37}{space 2} .2584988{col 48}{space 1}    0.18{col 57}{space 3}0.853{col 65}{space 4}-.4589313{col 78}{space 3} .5544251
{txt}{space 17}Italy  {c |}{col 25}{res}{space 2} .2082302{col 37}{space 2} .2338067{col 48}{space 1}    0.89{col 57}{space 3}0.373{col 65}{space 4}-.2500496{col 78}{space 3} .6665101
{txt}{space 11}New_Zealand  {c |}{col 25}{res}{space 2}        0{col 37}{txt}  (omitted)
{space 17}Spain  {c |}{col 25}{res}{space 2}        0{col 37}{txt}  (omitted)
{space 16}Sweden  {c |}{col 25}{res}{space 2} .0261223{col 37}{space 2} .0402879{col 48}{space 1}    0.65{col 57}{space 3}0.517{col 65}{space 4}-.0528451{col 78}{space 3} .1050898
{txt}{space 20}UK  {c |}{col 25}{res}{space 2}        0{col 37}{txt}  (omitted)
{space 20}US  {c |}{col 25}{res}{space 2}        0{col 37}{txt}  (omitted)
{space 23} {c |}
{space 18}_cons {c |}{col 25}{res}{space 2} .2910859{col 37}{space 2} .0725382{col 48}{space 1}    4.01{col 57}{space 3}0.000{col 65}{space 4} .1489054{col 78}{space 3} .4332665
{txt}{hline 24}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{help ivreg2##idtest:Underidentification test} (Kleibergen-Paap rk LM statistic):{res}{col 71}  11.726
{txt}{col 52}Chi-sq({res}1{txt}) P-val =  {res}{col 73}0.0006
{txt}{hline 78}
{help ivreg2##widtest:Weak identification test} (Cragg-Donald Wald F statistic):{res}{col 71}  11.676
{txt}                         (Kleibergen-Paap rk Wald F statistic):{res}{col 71}  11.726
{txt}Stock-Yogo weak ID test critical values:{res}{txt}{col 42}10% maximal IV size{res}{col 73} 16.38
{txt}{col 42}15% maximal IV size{res}{col 73}  8.96
{txt}{col 42}20% maximal IV size{res}{col 73}  6.66
{txt}{col 42}25% maximal IV size{res}{col 73}  5.53
{txt}Source: Stock-Yogo (2005).  Reproduced by permission.
NB: Critical values are for Cragg-Donald F statistic and i.i.d. errors.
{hline 78}
{help ivreg2##overidtests:Hansen J statistic} (overidentification test of all instruments):{res}{col 71}   0.000
{txt}{col 50}(equation exactly identified)
{hline 78}
Instrumented:{col 23}satis_head
Included instruments:{col 23}thirties fourties fifties sixties seventies
{col 23}income2quartile income3quartile income4quartile
{col 23}incomenoanswer female highschool college noreligion
{col 23}christiannotcatholic catholic fulltimeworker
{col 23}parttimeworker unemployed selfemployed outofLF goodhealth
{col 23}white black latino asian whitecollar bluecollar
{col 23}serviceworker mv_incomenoanswer mv_highschool
{col 23}mv_noreligion mv_parttimeworker mv_selfemployed
{col 23}mv_goodhealth mv_white mv_whitecollar 2.country 5.country
{col 23}6.country 10.country
Excluded instruments:{col 23}treatc
Dropped collinear:{col 23}mv_thirties mv_fourties mv_fifties mv_sixties
{col 23}mv_seventies mv_income2quartile mv_income3quartile
{col 23}mv_income4quartile mv_female mv_college
{col 23}mv_christiannotcatholic mv_catholic mv_fulltimeworker
{col 23}mv_unemployed mv_outofLF mv_black mv_latino mv_asian
{col 23}mv_bluecollar mv_serviceworker 4.country 7.country
{col 23}9.country 11.country 12.country
{hline 78}
({res}est2{txt} stored)

{com}. 
. estadd scalar fstat = e(widstat)

{txt}added scalar:
              e(fstat) =  {res}11.726073
{txt}
{com}. estadd local CFE "X", replace

{txt}added macro:
                e(CFE) : "{res:X}"

{com}. estadd local Controls "X", replace

{txt}added macro:
           e(Controls) : "{res:X}"

{com}. estadd local IV "SumIV", replace

{txt}added macro:
                 e(IV) : "{res:SumIV}"

{com}. get_mean

{txt}added scalar:
                 e(av) =  {res}.305
{txt}
{com}. 
. /* E.10. Col 3 */
. eststo: ivreg2 experts ///
>         (satis_head = TH1_TE1 TH1_TE2 TH1_TE3 TH1_TE4 TH2_TE1 TH2_TE2 TH2_TE3 TH2_TE4 TH3_TE1 TH3_TE2 TH3_TE3 TH3_TE4 TH4_TE1 TH4_TE2 TH4_TE3) $controls $mv_controls i.country, small robust 
{txt}Warning - collinearities detected
Vars dropped:{col 21}mv_thirties mv_fourties mv_fifties mv_sixties mv_seventies
{col 21}mv_income2quartile mv_income3quartile mv_income4quartile
{col 21}mv_female mv_college mv_christiannotcatholic mv_catholic
{col 21}mv_fulltimeworker mv_unemployed mv_outofLF mv_black
{col 21}mv_latino mv_asian mv_bluecollar mv_serviceworker 4.country
{col 21}7.country 9.country 11.country 12.country
{res}
{txt}IV (2SLS) estimation
{hline 20}

Estimates efficient for homoskedasticity only
Statistics robust to heteroskedasticity

{col 55}Number of obs = {res}   20535
{txt}{col 55}F( 41, 20493) = {res}   31.70
{txt}{col 55}Prob > F      = {res}  0.0000
{txt}Total (centered) SS     = {res} 4989.096859{txt}{col 55}Centered R2   = {res}  0.0398
{txt}Total (uncentered) SS   = {res}       11991{txt}{col 55}Uncentered R2 = {res}  0.6005
{txt}Residual SS             = {res} 4790.286844{txt}{col 55}Root MSE      = {res}   .4835

{txt}{hline 24}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 25}{c |}{col 37}    Robust
{col 1}                experts{col 25}{c |} Coefficient{col 37}  std. err.{col 49}      t{col 57}   P>|t|{col 65}     [95% con{col 78}f. interval]
{hline 24}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 13}satis_head {c |}{col 25}{res}{space 2} .1329443{col 37}{space 2} .3171803{col 48}{space 1}    0.42{col 57}{space 3}0.675{col 65}{space 4}-.4887543{col 78}{space 3} .7546429
{txt}{space 15}thirties {c |}{col 25}{res}{space 2}-.0101655{col 37}{space 2} .0148148{col 48}{space 1}   -0.69{col 57}{space 3}0.493{col 65}{space 4}-.0392038{col 78}{space 3} .0188728
{txt}{space 15}fourties {c |}{col 25}{res}{space 2}-.0789472{col 37}{space 2} .0191268{col 48}{space 1}   -4.13{col 57}{space 3}0.000{col 65}{space 4}-.1164372{col 78}{space 3}-.0414571
{txt}{space 16}fifties {c |}{col 25}{res}{space 2}-.1171062{col 37}{space 2} .0186909{col 48}{space 1}   -6.27{col 57}{space 3}0.000{col 65}{space 4}-.1537418{col 78}{space 3}-.0804706
{txt}{space 16}sixties {c |}{col 25}{res}{space 2}-.1818261{col 37}{space 2} .0187636{col 48}{space 1}   -9.69{col 57}{space 3}0.000{col 65}{space 4}-.2186043{col 78}{space 3}-.1450479
{txt}{space 14}seventies {c |}{col 25}{res}{space 2}-.1969201{col 37}{space 2} .0163774{col 48}{space 1}  -12.02{col 57}{space 3}0.000{col 65}{space 4} -.229021{col 78}{space 3}-.1648191
{txt}{space 8}income2quartile {c |}{col 25}{res}{space 2} .0036517{col 37}{space 2} .0114262{col 48}{space 1}    0.32{col 57}{space 3}0.749{col 65}{space 4}-.0187446{col 78}{space 3} .0260481
{txt}{space 8}income3quartile {c |}{col 25}{res}{space 2}  .019458{col 37}{space 2} .0132635{col 48}{space 1}    1.47{col 57}{space 3}0.142{col 65}{space 4}-.0065395{col 78}{space 3} .0454556
{txt}{space 8}income4quartile {c |}{col 25}{res}{space 2}-.0100195{col 37}{space 2} .0178913{col 48}{space 1}   -0.56{col 57}{space 3}0.575{col 65}{space 4} -.045088{col 78}{space 3} .0250489
{txt}{space 9}incomenoanswer {c |}{col 25}{res}{space 2}-.0087326{col 37}{space 2} .0163002{col 48}{space 1}   -0.54{col 57}{space 3}0.592{col 65}{space 4}-.0406823{col 78}{space 3} .0232172
{txt}{space 17}female {c |}{col 25}{res}{space 2} .0404483{col 37}{space 2}  .008061{col 48}{space 1}    5.02{col 57}{space 3}0.000{col 65}{space 4} .0246482{col 78}{space 3} .0562485
{txt}{space 13}highschool {c |}{col 25}{res}{space 2}-.0441306{col 37}{space 2} .0116002{col 48}{space 1}   -3.80{col 57}{space 3}0.000{col 65}{space 4}-.0668678{col 78}{space 3}-.0213934
{txt}{space 16}college {c |}{col 25}{res}{space 2}-.0436829{col 37}{space 2} .0109166{col 48}{space 1}   -4.00{col 57}{space 3}0.000{col 65}{space 4}-.0650803{col 78}{space 3}-.0222854
{txt}{space 13}noreligion {c |}{col 25}{res}{space 2}-.0280963{col 37}{space 2} .0160235{col 48}{space 1}   -1.75{col 57}{space 3}0.080{col 65}{space 4}-.0595037{col 78}{space 3} .0033111
{txt}{space 3}christiannotcatholic {c |}{col 25}{res}{space 2}-.0721857{col 37}{space 2} .0250294{col 48}{space 1}   -2.88{col 57}{space 3}0.004{col 65}{space 4}-.1212454{col 78}{space 3} -.023126
{txt}{space 15}catholic {c |}{col 25}{res}{space 2}-.0248164{col 37}{space 2} .0166742{col 48}{space 1}   -1.49{col 57}{space 3}0.137{col 65}{space 4}-.0574992{col 78}{space 3} .0078665
{txt}{space 9}fulltimeworker {c |}{col 25}{res}{space 2} .0021261{col 37}{space 2} .1012171{col 48}{space 1}    0.02{col 57}{space 3}0.983{col 65}{space 4}-.1962673{col 78}{space 3} .2005196
{txt}{space 9}parttimeworker {c |}{col 25}{res}{space 2} -.028118{col 37}{space 2} .1041767{col 48}{space 1}   -0.27{col 57}{space 3}0.787{col 65}{space 4}-.2323127{col 78}{space 3} .1760766
{txt}{space 13}unemployed {c |}{col 25}{res}{space 2}-.0326463{col 37}{space 2} .1130233{col 48}{space 1}   -0.29{col 57}{space 3}0.773{col 65}{space 4}-.2541809{col 78}{space 3} .1888884
{txt}{space 11}selfemployed {c |}{col 25}{res}{space 2} .0179041{col 37}{space 2} .1268858{col 48}{space 1}    0.14{col 57}{space 3}0.888{col 65}{space 4}-.2308022{col 78}{space 3} .2666103
{txt}{space 16}outofLF {c |}{col 25}{res}{space 2}-.0095123{col 37}{space 2} .1055699{col 48}{space 1}   -0.09{col 57}{space 3}0.928{col 65}{space 4}-.2164376{col 78}{space 3} .1974131
{txt}{space 13}goodhealth {c |}{col 25}{res}{space 2}-.0151079{col 37}{space 2} .0175034{col 48}{space 1}   -0.86{col 57}{space 3}0.388{col 65}{space 4} -.049416{col 78}{space 3} .0192002
{txt}{space 18}white {c |}{col 25}{res}{space 2} .0714302{col 37}{space 2} .0799133{col 48}{space 1}    0.89{col 57}{space 3}0.371{col 65}{space 4}-.0852062{col 78}{space 3} .2280665
{txt}{space 18}black {c |}{col 25}{res}{space 2} .1208072{col 37}{space 2} .0742991{col 48}{space 1}    1.63{col 57}{space 3}0.104{col 65}{space 4}-.0248249{col 78}{space 3} .2664393
{txt}{space 17}latino {c |}{col 25}{res}{space 2} .1577888{col 37}{space 2} .0793559{col 48}{space 1}    1.99{col 57}{space 3}0.047{col 65}{space 4} .0022448{col 78}{space 3} .3133328
{txt}{space 18}asian {c |}{col 25}{res}{space 2} .2194765{col 37}{space 2} .0816867{col 48}{space 1}    2.69{col 57}{space 3}0.007{col 65}{space 4}  .059364{col 78}{space 3}  .379589
{txt}{space 12}whitecollar {c |}{col 25}{res}{space 2} .0257529{col 37}{space 2} .0202196{col 48}{space 1}    1.27{col 57}{space 3}0.203{col 65}{space 4} -.013879{col 78}{space 3} .0653849
{txt}{space 13}bluecollar {c |}{col 25}{res}{space 2} .0169244{col 37}{space 2} .0266783{col 48}{space 1}    0.63{col 57}{space 3}0.526{col 65}{space 4}-.0353672{col 78}{space 3}  .069216
{txt}{space 10}serviceworker {c |}{col 25}{res}{space 2} .0049464{col 37}{space 2} .0174515{col 48}{space 1}    0.28{col 57}{space 3}0.777{col 65}{space 4}  -.02926{col 78}{space 3} .0391528
{txt}{space 12}mv_thirties {c |}{col 25}{res}{space 2}        0{col 37}{txt}  (omitted)
{space 12}mv_fourties {c |}{col 25}{res}{space 2}        0{col 37}{txt}  (omitted)
{space 13}mv_fifties {c |}{col 25}{res}{space 2}        0{col 37}{txt}  (omitted)
{space 13}mv_sixties {c |}{col 25}{res}{space 2}        0{col 37}{txt}  (omitted)
{space 11}mv_seventies {c |}{col 25}{res}{space 2}        0{col 37}{txt}  (omitted)
{space 5}mv_income2quartile {c |}{col 25}{res}{space 2}        0{col 37}{txt}  (omitted)
{space 5}mv_income3quartile {c |}{col 25}{res}{space 2}        0{col 37}{txt}  (omitted)
{space 5}mv_income4quartile {c |}{col 25}{res}{space 2}        0{col 37}{txt}  (omitted)
{space 6}mv_incomenoanswer {c |}{col 25}{res}{space 2} .0810874{col 37}{space 2}  .031899{col 48}{space 1}    2.54{col 57}{space 3}0.011{col 65}{space 4} .0185628{col 78}{space 3} .1436121
{txt}{space 14}mv_female {c |}{col 25}{res}{space 2}        0{col 37}{txt}  (omitted)
{space 10}mv_highschool {c |}{col 25}{res}{space 2}-.0449879{col 37}{space 2} .0276573{col 48}{space 1}   -1.63{col 57}{space 3}0.104{col 65}{space 4}-.0991985{col 78}{space 3} .0092227
{txt}{space 13}mv_college {c |}{col 25}{res}{space 2}        0{col 37}{txt}  (omitted)
{space 10}mv_noreligion {c |}{col 25}{res}{space 2} .0253056{col 37}{space 2} .0386807{col 48}{space 1}    0.65{col 57}{space 3}0.513{col 65}{space 4}-.0505117{col 78}{space 3} .1011228
{txt}mv_christiannotcatholic {c |}{col 25}{res}{space 2}        0{col 37}{txt}  (omitted)
{space 12}mv_catholic {c |}{col 25}{res}{space 2}        0{col 37}{txt}  (omitted)
{space 6}mv_fulltimeworker {c |}{col 25}{res}{space 2}        0{col 37}{txt}  (omitted)
{space 6}mv_parttimeworker {c |}{col 25}{res}{space 2}-.0203378{col 37}{space 2} .0971659{col 48}{space 1}   -0.21{col 57}{space 3}0.834{col 65}{space 4}-.2107907{col 78}{space 3} .1701151
{txt}{space 10}mv_unemployed {c |}{col 25}{res}{space 2}        0{col 37}{txt}  (omitted)
{space 8}mv_selfemployed {c |}{col 25}{res}{space 2}-.2210859{col 37}{space 2} .0996733{col 48}{space 1}   -2.22{col 57}{space 3}0.027{col 65}{space 4}-.4164535{col 78}{space 3}-.0257184
{txt}{space 13}mv_outofLF {c |}{col 25}{res}{space 2}        0{col 37}{txt}  (omitted)
{space 10}mv_goodhealth {c |}{col 25}{res}{space 2} .1510494{col 37}{space 2} .1471038{col 48}{space 1}    1.03{col 57}{space 3}0.305{col 65}{space 4}-.1372858{col 78}{space 3} .4393845
{txt}{space 15}mv_white {c |}{col 25}{res}{space 2} .1074191{col 37}{space 2}  .138446{col 48}{space 1}    0.78{col 57}{space 3}0.438{col 65}{space 4}-.1639462{col 78}{space 3} .3787843
{txt}{space 15}mv_black {c |}{col 25}{res}{space 2}        0{col 37}{txt}  (omitted)
{space 14}mv_latino {c |}{col 25}{res}{space 2}        0{col 37}{txt}  (omitted)
{space 15}mv_asian {c |}{col 25}{res}{space 2}        0{col 37}{txt}  (omitted)
{space 9}mv_whitecollar {c |}{col 25}{res}{space 2} -.069872{col 37}{space 2} .1003183{col 48}{space 1}   -0.70{col 57}{space 3}0.486{col 65}{space 4}-.2665039{col 78}{space 3} .1267599
{txt}{space 10}mv_bluecollar {c |}{col 25}{res}{space 2}        0{col 37}{txt}  (omitted)
{space 7}mv_serviceworker {c |}{col 25}{res}{space 2}        0{col 37}{txt}  (omitted)
{space 23} {c |}
{space 16}country {c |}
{space 15}Austria  {c |}{col 25}{res}{space 2} .1653057{col 37}{space 2} .1799698{col 48}{space 1}    0.92{col 57}{space 3}0.358{col 65}{space 4}-.1874494{col 78}{space 3} .5180608
{txt}{space 16}France  {c |}{col 25}{res}{space 2}        0{col 37}{txt}  (omitted)
{space 15}Germany  {c |}{col 25}{res}{space 2}-.0219547{col 37}{space 2} .1922081{col 48}{space 1}   -0.11{col 57}{space 3}0.909{col 65}{space 4} -.398698{col 78}{space 3} .3547886
{txt}{space 17}Italy  {c |}{col 25}{res}{space 2}-.0235938{col 37}{space 2} .1758885{col 48}{space 1}   -0.13{col 57}{space 3}0.893{col 65}{space 4}-.3683492{col 78}{space 3} .3211616
{txt}{space 11}New_Zealand  {c |}{col 25}{res}{space 2}        0{col 37}{txt}  (omitted)
{space 17}Spain  {c |}{col 25}{res}{space 2}        0{col 37}{txt}  (omitted)
{space 16}Sweden  {c |}{col 25}{res}{space 2} -.182824{col 37}{space 2} .0387281{col 48}{space 1}   -4.72{col 57}{space 3}0.000{col 65}{space 4}-.2587343{col 78}{space 3}-.1069138
{txt}{space 20}UK  {c |}{col 25}{res}{space 2}        0{col 37}{txt}  (omitted)
{space 20}US  {c |}{col 25}{res}{space 2}        0{col 37}{txt}  (omitted)
{space 23} {c |}
{space 18}_cons {c |}{col 25}{res}{space 2}  .851896{col 37}{space 2} .0781459{col 48}{space 1}   10.90{col 57}{space 3}0.000{col 65}{space 4} .6987238{col 78}{space 3} 1.005068
{txt}{hline 24}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{help ivreg2##idtest:Underidentification test} (Kleibergen-Paap rk LM statistic):{res}{col 71}  28.501
{txt}{col 52}Chi-sq({res}15{txt}) P-val =  {res}{col 73}0.0186
{txt}{hline 78}
{help ivreg2##widtest:Weak identification test} (Cragg-Donald Wald F statistic):{res}{col 71}   1.863
{txt}                         (Kleibergen-Paap rk Wald F statistic):{res}{col 71}   1.905
{txt}Stock-Yogo weak ID test critical values:{res}{txt}{col 42} 5% maximal IV relative bias{res}{col 73} 21.23
{txt}{col 42}10% maximal IV relative bias{res}{col 73} 11.51
{txt}{col 42}20% maximal IV relative bias{res}{col 73}  6.42
{txt}{col 42}30% maximal IV relative bias{res}{col 73}  4.63
{txt}{col 42}10% maximal IV size{res}{col 73} 50.39
{txt}{col 42}15% maximal IV size{res}{col 73} 26.80
{txt}{col 42}20% maximal IV size{res}{col 73} 18.72
{txt}{col 42}25% maximal IV size{res}{col 73} 14.60
{txt}Source: Stock-Yogo (2005).  Reproduced by permission.
NB: Critical values are for Cragg-Donald F statistic and i.i.d. errors.
{hline 78}
{help ivreg2##overidtests:Hansen J statistic} (overidentification test of all instruments):{res}{col 71}  15.610
{txt}{col 52}Chi-sq({res}14{txt}) P-val =  {res}{col 73}0.3377
{txt}{hline 78}
Instrumented:{col 23}satis_head
Included instruments:{col 23}thirties fourties fifties sixties seventies
{col 23}income2quartile income3quartile income4quartile
{col 23}incomenoanswer female highschool college noreligion
{col 23}christiannotcatholic catholic fulltimeworker
{col 23}parttimeworker unemployed selfemployed outofLF goodhealth
{col 23}white black latino asian whitecollar bluecollar
{col 23}serviceworker mv_incomenoanswer mv_highschool
{col 23}mv_noreligion mv_parttimeworker mv_selfemployed
{col 23}mv_goodhealth mv_white mv_whitecollar 2.country 5.country
{col 23}6.country 10.country
Excluded instruments:{col 23}TH1_TE1 TH1_TE2 TH1_TE3 TH1_TE4 TH2_TE1 TH2_TE2 TH2_TE3
{col 23}TH2_TE4 TH3_TE1 TH3_TE2 TH3_TE3 TH3_TE4 TH4_TE1 TH4_TE2
{col 23}TH4_TE3
Dropped collinear:{col 23}mv_thirties mv_fourties mv_fifties mv_sixties
{col 23}mv_seventies mv_income2quartile mv_income3quartile
{col 23}mv_income4quartile mv_female mv_college
{col 23}mv_christiannotcatholic mv_catholic mv_fulltimeworker
{col 23}mv_unemployed mv_outofLF mv_black mv_latino mv_asian
{col 23}mv_bluecollar mv_serviceworker 4.country 7.country
{col 23}9.country 11.country 12.country
{hline 78}
({res}est3{txt} stored)

{com}. 
. estadd scalar fstat = e(widstat)

{txt}added scalar:
              e(fstat) =  {res}1.9046447
{txt}
{com}. estadd local CFE "X", replace

{txt}added macro:
                e(CFE) : "{res:X}"

{com}. estadd local IV "16 IVs", replace

{txt}added macro:
                 e(IV) : "{res:16 IVs}"

{com}. estadd local Controls "X", replace

{txt}added macro:
           e(Controls) : "{res:X}"

{com}. get_mean

{txt}added scalar:
                 e(av) =  {res}.584
{txt}
{com}. 
. /* E.10. Col 4 */
. eststo: ivreg2 experts ///
>         (satis_head = treatc ) $controls $mv_controls i.country, small robust 
{txt}Warning - collinearities detected
Vars dropped:{col 21}mv_thirties mv_fourties mv_fifties mv_sixties mv_seventies
{col 21}mv_income2quartile mv_income3quartile mv_income4quartile
{col 21}mv_female mv_college mv_christiannotcatholic mv_catholic
{col 21}mv_fulltimeworker mv_unemployed mv_outofLF mv_black
{col 21}mv_latino mv_asian mv_bluecollar mv_serviceworker 4.country
{col 21}7.country 9.country 11.country 12.country
{res}
{txt}IV (2SLS) estimation
{hline 20}

Estimates efficient for homoskedasticity only
Statistics robust to heteroskedasticity

{col 55}Number of obs = {res}   20535
{txt}{col 55}F( 41, 20493) = {res}   32.11
{txt}{col 55}Prob > F      = {res}  0.0000
{txt}Total (centered) SS     = {res} 4989.096859{txt}{col 55}Centered R2   = {res}  0.0489
{txt}Total (uncentered) SS   = {res}       11991{txt}{col 55}Uncentered R2 = {res}  0.6043
{txt}Residual SS             = {res} 4745.015142{txt}{col 55}Root MSE      = {res}   .4812

{txt}{hline 24}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 25}{c |}{col 37}    Robust
{col 1}                experts{col 25}{c |} Coefficient{col 37}  std. err.{col 49}      t{col 57}   P>|t|{col 65}     [95% con{col 78}f. interval]
{hline 24}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 13}satis_head {c |}{col 25}{res}{space 2} .0584525{col 37}{space 2} .4857455{col 48}{space 1}    0.12{col 57}{space 3}0.904{col 65}{space 4}-.8936473{col 78}{space 3} 1.010552
{txt}{space 15}thirties {c |}{col 25}{res}{space 2}-.0122359{col 37}{space 2} .0179514{col 48}{space 1}   -0.68{col 57}{space 3}0.495{col 65}{space 4}-.0474221{col 78}{space 3} .0229503
{txt}{space 15}fourties {c |}{col 25}{res}{space 2}-.0824668{col 37}{space 2}  .025816{col 48}{space 1}   -3.19{col 57}{space 3}0.001{col 65}{space 4}-.1330682{col 78}{space 3}-.0318654
{txt}{space 16}fifties {c |}{col 25}{res}{space 2}-.1203835{col 37}{space 2} .0246739{col 48}{space 1}   -4.88{col 57}{space 3}0.000{col 65}{space 4}-.1687463{col 78}{space 3}-.0720207
{txt}{space 16}sixties {c |}{col 25}{res}{space 2} -.185135{col 37}{space 2} .0248216{col 48}{space 1}   -7.46{col 57}{space 3}0.000{col 65}{space 4}-.2337874{col 78}{space 3}-.1364826
{txt}{space 14}seventies {c |}{col 25}{res}{space 2}-.1986826{col 37}{space 2} .0184703{col 48}{space 1}  -10.76{col 57}{space 3}0.000{col 65}{space 4}-.2348859{col 78}{space 3}-.1624793
{txt}{space 8}income2quartile {c |}{col 25}{res}{space 2} .0049626{col 37}{space 2} .0130892{col 48}{space 1}    0.38{col 57}{space 3}0.705{col 65}{space 4}-.0206933{col 78}{space 3} .0306185
{txt}{space 8}income3quartile {c |}{col 25}{res}{space 2} .0214075{col 37}{space 2} .0163435{col 48}{space 1}    1.31{col 57}{space 3}0.190{col 65}{space 4}-.0106271{col 78}{space 3} .0534421
{txt}{space 8}income4quartile {c |}{col 25}{res}{space 2}-.0066198{col 37}{space 2} .0244725{col 48}{space 1}   -0.27{col 57}{space 3}0.787{col 65}{space 4}-.0545879{col 78}{space 3} .0413482
{txt}{space 9}incomenoanswer {c |}{col 25}{res}{space 2}-.0094288{col 37}{space 2} .0166219{col 48}{space 1}   -0.57{col 57}{space 3}0.571{col 65}{space 4}-.0420092{col 78}{space 3} .0231515
{txt}{space 17}female {c |}{col 25}{res}{space 2} .0414318{col 37}{space 2}  .009407{col 48}{space 1}    4.40{col 57}{space 3}0.000{col 65}{space 4} .0229933{col 78}{space 3} .0598703
{txt}{space 13}highschool {c |}{col 25}{res}{space 2}-.0437657{col 37}{space 2} .0116984{col 48}{space 1}   -3.74{col 57}{space 3}0.000{col 65}{space 4}-.0666954{col 78}{space 3} -.020836
{txt}{space 16}college {c |}{col 25}{res}{space 2}-.0432458{col 37}{space 2} .0110952{col 48}{space 1}   -3.90{col 57}{space 3}0.000{col 65}{space 4}-.0649933{col 78}{space 3}-.0214984
{txt}{space 13}noreligion {c |}{col 25}{res}{space 2}-.0298336{col 37}{space 2} .0181287{col 48}{space 1}   -1.65{col 57}{space 3}0.100{col 65}{space 4}-.0653673{col 78}{space 3} .0057001
{txt}{space 3}christiannotcatholic {c |}{col 25}{res}{space 2}-.0677411{col 37}{space 2} .0332811{col 48}{space 1}   -2.04{col 57}{space 3}0.042{col 65}{space 4}-.1329746{col 78}{space 3}-.0025075
{txt}{space 15}catholic {c |}{col 25}{res}{space 2}-.0229746{col 37}{space 2} .0189747{col 48}{space 1}   -1.21{col 57}{space 3}0.226{col 65}{space 4}-.0601665{col 78}{space 3} .0142174
{txt}{space 9}fulltimeworker {c |}{col 25}{res}{space 2}-.0212655{col 37}{space 2} .1536321{col 48}{space 1}   -0.14{col 57}{space 3}0.890{col 65}{space 4}-.3223967{col 78}{space 3} .2798657
{txt}{space 9}parttimeworker {c |}{col 25}{res}{space 2}-.0510384{col 37}{space 2} .1535923{col 48}{space 1}   -0.33{col 57}{space 3}0.740{col 65}{space 4}-.3520916{col 78}{space 3} .2500148
{txt}{space 13}unemployed {c |}{col 25}{res}{space 2} -.058444{col 37}{space 2} .1703355{col 48}{space 1}   -0.34{col 57}{space 3}0.732{col 65}{space 4}-.3923152{col 78}{space 3} .2754272
{txt}{space 11}selfemployed {c |}{col 25}{res}{space 2}-.0096348{col 37}{space 2} .1861556{col 48}{space 1}   -0.05{col 57}{space 3}0.959{col 65}{space 4}-.3745146{col 78}{space 3}  .355245
{txt}{space 16}outofLF {c |}{col 25}{res}{space 2}-.0339025{col 37}{space 2}  .160199{col 48}{space 1}   -0.21{col 57}{space 3}0.832{col 65}{space 4}-.3479053{col 78}{space 3} .2801004
{txt}{space 13}goodhealth {c |}{col 25}{res}{space 2}-.0113901{col 37}{space 2} .0253667{col 48}{space 1}   -0.45{col 57}{space 3}0.653{col 65}{space 4}-.0611108{col 78}{space 3} .0383306
{txt}{space 18}white {c |}{col 25}{res}{space 2} .0813097{col 37}{space 2} .0928884{col 48}{space 1}    0.88{col 57}{space 3}0.381{col 65}{space 4} -.100759{col 78}{space 3} .2633784
{txt}{space 18}black {c |}{col 25}{res}{space 2} .1217947{col 37}{space 2} .0733125{col 48}{space 1}    1.66{col 57}{space 3}0.097{col 65}{space 4}-.0219036{col 78}{space 3}  .265493
{txt}{space 17}latino {c |}{col 25}{res}{space 2} .1603198{col 37}{space 2} .0794302{col 48}{space 1}    2.02{col 57}{space 3}0.044{col 65}{space 4} .0046302{col 78}{space 3} .3160095
{txt}{space 18}asian {c |}{col 25}{res}{space 2} .2247262{col 37}{space 2} .0843866{col 48}{space 1}    2.66{col 57}{space 3}0.008{col 65}{space 4} .0593218{col 78}{space 3} .3901306
{txt}{space 12}whitecollar {c |}{col 25}{res}{space 2} .0243183{col 37}{space 2} .0213491{col 48}{space 1}    1.14{col 57}{space 3}0.255{col 65}{space 4}-.0175276{col 78}{space 3} .0661642
{txt}{space 13}bluecollar {c |}{col 25}{res}{space 2}  .012859{col 37}{space 2} .0333586{col 48}{space 1}    0.39{col 57}{space 3}0.700{col 65}{space 4}-.0525265{col 78}{space 3} .0782445
{txt}{space 10}serviceworker {c |}{col 25}{res}{space 2} .0033054{col 37}{space 2} .0191756{col 48}{space 1}    0.17{col 57}{space 3}0.863{col 65}{space 4}-.0342803{col 78}{space 3}  .040891
{txt}{space 12}mv_thirties {c |}{col 25}{res}{space 2}        0{col 37}{txt}  (omitted)
{space 12}mv_fourties {c |}{col 25}{res}{space 2}        0{col 37}{txt}  (omitted)
{space 13}mv_fifties {c |}{col 25}{res}{space 2}        0{col 37}{txt}  (omitted)
{space 13}mv_sixties {c |}{col 25}{res}{space 2}        0{col 37}{txt}  (omitted)
{space 11}mv_seventies {c |}{col 25}{res}{space 2}        0{col 37}{txt}  (omitted)
{space 5}mv_income2quartile {c |}{col 25}{res}{space 2}        0{col 37}{txt}  (omitted)
{space 5}mv_income3quartile {c |}{col 25}{res}{space 2}        0{col 37}{txt}  (omitted)
{space 5}mv_income4quartile {c |}{col 25}{res}{space 2}        0{col 37}{txt}  (omitted)
{space 6}mv_incomenoanswer {c |}{col 25}{res}{space 2} .0755944{col 37}{space 2} .0419328{col 48}{space 1}    1.80{col 57}{space 3}0.071{col 65}{space 4}-.0065973{col 78}{space 3} .1577861
{txt}{space 14}mv_female {c |}{col 25}{res}{space 2}        0{col 37}{txt}  (omitted)
{space 10}mv_highschool {c |}{col 25}{res}{space 2}-.0457103{col 37}{space 2} .0277368{col 48}{space 1}   -1.65{col 57}{space 3}0.099{col 65}{space 4}-.1000765{col 78}{space 3}  .008656
{txt}{space 13}mv_college {c |}{col 25}{res}{space 2}        0{col 37}{txt}  (omitted)
{space 10}mv_noreligion {c |}{col 25}{res}{space 2} .0260811{col 37}{space 2} .0388341{col 48}{space 1}    0.67{col 57}{space 3}0.502{col 65}{space 4}-.0500369{col 78}{space 3}  .102199
{txt}mv_christiannotcatholic {c |}{col 25}{res}{space 2}        0{col 37}{txt}  (omitted)
{space 12}mv_catholic {c |}{col 25}{res}{space 2}        0{col 37}{txt}  (omitted)
{space 6}mv_fulltimeworker {c |}{col 25}{res}{space 2}        0{col 37}{txt}  (omitted)
{space 6}mv_parttimeworker {c |}{col 25}{res}{space 2}-.0419748{col 37}{space 2} .1443971{col 48}{space 1}   -0.29{col 57}{space 3}0.771{col 65}{space 4}-.3250046{col 78}{space 3}  .241055
{txt}{space 10}mv_unemployed {c |}{col 25}{res}{space 2}        0{col 37}{txt}  (omitted)
{space 8}mv_selfemployed {c |}{col 25}{res}{space 2}-.1989243{col 37}{space 2} .1480092{col 48}{space 1}   -1.34{col 57}{space 3}0.179{col 65}{space 4}-.4890341{col 78}{space 3} .0911856
{txt}{space 13}mv_outofLF {c |}{col 25}{res}{space 2}        0{col 37}{txt}  (omitted)
{space 10}mv_goodhealth {c |}{col 25}{res}{space 2} .1650235{col 37}{space 2} .1606443{col 48}{space 1}    1.03{col 57}{space 3}0.304{col 65}{space 4}-.1498521{col 78}{space 3} .4798991
{txt}{space 15}mv_white {c |}{col 25}{res}{space 2} .1357062{col 37}{space 2} .1964204{col 48}{space 1}    0.69{col 57}{space 3}0.490{col 65}{space 4}-.2492935{col 78}{space 3} .5207059
{txt}{space 15}mv_black {c |}{col 25}{res}{space 2}        0{col 37}{txt}  (omitted)
{space 14}mv_latino {c |}{col 25}{res}{space 2}        0{col 37}{txt}  (omitted)
{space 15}mv_asian {c |}{col 25}{res}{space 2}        0{col 37}{txt}  (omitted)
{space 9}mv_whitecollar {c |}{col 25}{res}{space 2}-.0477972{col 37}{space 2} .1481018{col 48}{space 1}   -0.32{col 57}{space 3}0.747{col 65}{space 4}-.3380885{col 78}{space 3} .2424941
{txt}{space 10}mv_bluecollar {c |}{col 25}{res}{space 2}        0{col 37}{txt}  (omitted)
{space 7}mv_serviceworker {c |}{col 25}{res}{space 2}        0{col 37}{txt}  (omitted)
{space 23} {c |}
{space 16}country {c |}
{space 15}Austria  {c |}{col 25}{res}{space 2} .2036257{col 37}{space 2} .2608015{col 48}{space 1}    0.78{col 57}{space 3}0.435{col 65}{space 4}-.3075661{col 78}{space 3} .7148174
{txt}{space 16}France  {c |}{col 25}{res}{space 2}        0{col 37}{txt}  (omitted)
{space 15}Germany  {c |}{col 25}{res}{space 2} .0195563{col 37}{space 2} .2807612{col 48}{space 1}    0.07{col 57}{space 3}0.944{col 65}{space 4}-.5307582{col 78}{space 3} .5698707
{txt}{space 17}Italy  {c |}{col 25}{res}{space 2} .0127518{col 37}{space 2}  .251333{col 48}{space 1}    0.05{col 57}{space 3}0.960{col 65}{space 4}-.4798809{col 78}{space 3} .5053844
{txt}{space 11}New_Zealand  {c |}{col 25}{res}{space 2}        0{col 37}{txt}  (omitted)
{space 17}Spain  {c |}{col 25}{res}{space 2}        0{col 37}{txt}  (omitted)
{space 16}Sweden  {c |}{col 25}{res}{space 2}-.1785959{col 37}{space 2} .0438096{col 48}{space 1}   -4.08{col 57}{space 3}0.000{col 65}{space 4}-.2644662{col 78}{space 3}-.0927256
{txt}{space 20}UK  {c |}{col 25}{res}{space 2}        0{col 37}{txt}  (omitted)
{space 20}US  {c |}{col 25}{res}{space 2}        0{col 37}{txt}  (omitted)
{space 23} {c |}
{space 18}_cons {c |}{col 25}{res}{space 2} .8522221{col 37}{space 2} .0769887{col 48}{space 1}   11.07{col 57}{space 3}0.000{col 65}{space 4}  .701318{col 78}{space 3} 1.003126
{txt}{hline 24}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{help ivreg2##idtest:Underidentification test} (Kleibergen-Paap rk LM statistic):{res}{col 71}  11.806
{txt}{col 52}Chi-sq({res}1{txt}) P-val =  {res}{col 73}0.0006
{txt}{hline 78}
{help ivreg2##widtest:Weak identification test} (Cragg-Donald Wald F statistic):{res}{col 71}  11.754
{txt}                         (Kleibergen-Paap rk Wald F statistic):{res}{col 71}  11.806
{txt}Stock-Yogo weak ID test critical values:{res}{txt}{col 42}10% maximal IV size{res}{col 73} 16.38
{txt}{col 42}15% maximal IV size{res}{col 73}  8.96
{txt}{col 42}20% maximal IV size{res}{col 73}  6.66
{txt}{col 42}25% maximal IV size{res}{col 73}  5.53
{txt}Source: Stock-Yogo (2005).  Reproduced by permission.
NB: Critical values are for Cragg-Donald F statistic and i.i.d. errors.
{hline 78}
{help ivreg2##overidtests:Hansen J statistic} (overidentification test of all instruments):{res}{col 71}   0.000
{txt}{col 50}(equation exactly identified)
{hline 78}
Instrumented:{col 23}satis_head
Included instruments:{col 23}thirties fourties fifties sixties seventies
{col 23}income2quartile income3quartile income4quartile
{col 23}incomenoanswer female highschool college noreligion
{col 23}christiannotcatholic catholic fulltimeworker
{col 23}parttimeworker unemployed selfemployed outofLF goodhealth
{col 23}white black latino asian whitecollar bluecollar
{col 23}serviceworker mv_incomenoanswer mv_highschool
{col 23}mv_noreligion mv_parttimeworker mv_selfemployed
{col 23}mv_goodhealth mv_white mv_whitecollar 2.country 5.country
{col 23}6.country 10.country
Excluded instruments:{col 23}treatc
Dropped collinear:{col 23}mv_thirties mv_fourties mv_fifties mv_sixties
{col 23}mv_seventies mv_income2quartile mv_income3quartile
{col 23}mv_income4quartile mv_female mv_college
{col 23}mv_christiannotcatholic mv_catholic mv_fulltimeworker
{col 23}mv_unemployed mv_outofLF mv_black mv_latino mv_asian
{col 23}mv_bluecollar mv_serviceworker 4.country 7.country
{col 23}9.country 11.country 12.country
{hline 78}
({res}est4{txt} stored)

{com}. 
. estadd scalar fstat = e(widstat)

{txt}added scalar:
              e(fstat) =  {res}11.806277
{txt}
{com}. estadd local CFE "X", replace

{txt}added macro:
                e(CFE) : "{res:X}"

{com}. estadd local IV "SumIV", replace

{txt}added macro:
                 e(IV) : "{res:SumIV}"

{com}. estadd local Controls "X", replace

{txt}added macro:
           e(Controls) : "{res:X}"

{com}. get_mean

{txt}added scalar:
                 e(av) =  {res}.584
{txt}
{com}. 
. /* E.10. Col 5 */
. eststo: ivreg2 army ///
>         (satis_head = TH1_TE1 TH1_TE2 TH1_TE3 TH1_TE4 TH2_TE1 TH2_TE2 TH2_TE3 TH2_TE4 TH3_TE1 TH3_TE2 TH3_TE3 TH3_TE4 TH4_TE1 TH4_TE2 TH4_TE3) $controls $mv_controls i.country, small robust 
{txt}Warning - collinearities detected
Vars dropped:{col 21}mv_thirties mv_fourties mv_fifties mv_sixties mv_seventies
{col 21}mv_income2quartile mv_income3quartile mv_income4quartile
{col 21}mv_female mv_college mv_christiannotcatholic mv_catholic
{col 21}mv_fulltimeworker mv_unemployed mv_outofLF mv_black
{col 21}mv_latino mv_asian mv_bluecollar mv_serviceworker 4.country
{col 21}7.country 9.country 11.country 12.country
{res}
{txt}IV (2SLS) estimation
{hline 20}

Estimates efficient for homoskedasticity only
Statistics robust to heteroskedasticity

{col 55}Number of obs = {res}   20534
{txt}{col 55}F( 41, 20492) = {res}   31.93
{txt}{col 55}Prob > F      = {res}  0.0000
{txt}Total (centered) SS     = {res} 2876.986461{txt}{col 55}Centered R2   = {res} -0.0448
{txt}Total (uncentered) SS   = {res}        3460{txt}{col 55}Uncentered R2 = {res}  0.1313
{txt}Residual SS             = {res} 3005.836401{txt}{col 55}Root MSE      = {res}    .383

{txt}{hline 24}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 25}{c |}{col 37}    Robust
{col 1}                   army{col 25}{c |} Coefficient{col 37}  std. err.{col 49}      t{col 57}   P>|t|{col 65}     [95% con{col 78}f. interval]
{hline 24}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 13}satis_head {c |}{col 25}{res}{space 2}  -.39537{col 37}{space 2} .2457236{col 48}{space 1}   -1.61{col 57}{space 3}0.108{col 65}{space 4}-.8770079{col 78}{space 3} .0862678
{txt}{space 15}thirties {c |}{col 25}{res}{space 2} -.001276{col 37}{space 2} .0133046{col 48}{space 1}   -0.10{col 57}{space 3}0.924{col 65}{space 4}-.0273541{col 78}{space 3} .0248021
{txt}{space 15}fourties {c |}{col 25}{res}{space 2}-.0741307{col 37}{space 2} .0159623{col 48}{space 1}   -4.64{col 57}{space 3}0.000{col 65}{space 4} -.105418{col 78}{space 3}-.0428433
{txt}{space 16}fifties {c |}{col 25}{res}{space 2}-.1440498{col 37}{space 2} .0151138{col 48}{space 1}   -9.53{col 57}{space 3}0.000{col 65}{space 4}-.1736741{col 78}{space 3}-.1144256
{txt}{space 16}sixties {c |}{col 25}{res}{space 2}-.1895041{col 37}{space 2} .0150056{col 48}{space 1}  -12.63{col 57}{space 3}0.000{col 65}{space 4}-.2189164{col 78}{space 3}-.1600919
{txt}{space 14}seventies {c |}{col 25}{res}{space 2}-.1859181{col 37}{space 2}  .012776{col 48}{space 1}  -14.55{col 57}{space 3}0.000{col 65}{space 4}-.2109601{col 78}{space 3}-.1608761
{txt}{space 8}income2quartile {c |}{col 25}{res}{space 2}-.0189676{col 37}{space 2} .0092579{col 48}{space 1}   -2.05{col 57}{space 3}0.040{col 65}{space 4}-.0371138{col 78}{space 3}-.0008214
{txt}{space 8}income3quartile {c |}{col 25}{res}{space 2}-.0165556{col 37}{space 2} .0105263{col 48}{space 1}   -1.57{col 57}{space 3}0.116{col 65}{space 4}-.0371881{col 78}{space 3} .0040768
{txt}{space 8}income4quartile {c |}{col 25}{res}{space 2} -.026526{col 37}{space 2} .0140288{col 48}{space 1}   -1.89{col 57}{space 3}0.059{col 65}{space 4}-.0540235{col 78}{space 3} .0009716
{txt}{space 9}incomenoanswer {c |}{col 25}{res}{space 2}-.0141108{col 37}{space 2} .0127153{col 48}{space 1}   -1.11{col 57}{space 3}0.267{col 65}{space 4}-.0390338{col 78}{space 3} .0108122
{txt}{space 17}female {c |}{col 25}{res}{space 2} .0031489{col 37}{space 2} .0063541{col 48}{space 1}    0.50{col 57}{space 3}0.620{col 65}{space 4}-.0093058{col 78}{space 3} .0156035
{txt}{space 13}highschool {c |}{col 25}{res}{space 2}-.0558714{col 37}{space 2} .0096052{col 48}{space 1}   -5.82{col 57}{space 3}0.000{col 65}{space 4}-.0746985{col 78}{space 3}-.0370444
{txt}{space 16}college {c |}{col 25}{res}{space 2}-.0956022{col 37}{space 2} .0089002{col 48}{space 1}  -10.74{col 57}{space 3}0.000{col 65}{space 4}-.1130473{col 78}{space 3} -.078157
{txt}{space 13}noreligion {c |}{col 25}{res}{space 2}-.1225841{col 37}{space 2} .0148771{col 48}{space 1}   -8.24{col 57}{space 3}0.000{col 65}{space 4}-.1517444{col 78}{space 3}-.0934238
{txt}{space 3}christiannotcatholic {c |}{col 25}{res}{space 2}-.0398884{col 37}{space 2} .0208307{col 48}{space 1}   -1.91{col 57}{space 3}0.056{col 65}{space 4}-.0807182{col 78}{space 3} .0009413
{txt}{space 15}catholic {c |}{col 25}{res}{space 2}-.0274011{col 37}{space 2}  .015104{col 48}{space 1}   -1.81{col 57}{space 3}0.070{col 65}{space 4}-.0570062{col 78}{space 3} .0022039
{txt}{space 9}fulltimeworker {c |}{col 25}{res}{space 2}-.1448841{col 37}{space 2} .0786406{col 48}{space 1}   -1.84{col 57}{space 3}0.065{col 65}{space 4} -.299026{col 78}{space 3} .0092579
{txt}{space 9}parttimeworker {c |}{col 25}{res}{space 2}-.1262127{col 37}{space 2} .0834074{col 48}{space 1}   -1.51{col 57}{space 3}0.130{col 65}{space 4}-.2896979{col 78}{space 3} .0372725
{txt}{space 13}unemployed {c |}{col 25}{res}{space 2}-.1841083{col 37}{space 2} .0880121{col 48}{space 1}   -2.09{col 57}{space 3}0.036{col 65}{space 4} -.356619{col 78}{space 3}-.0115975
{txt}{space 11}selfemployed {c |}{col 25}{res}{space 2}-.1716323{col 37}{space 2} .0990159{col 48}{space 1}   -1.73{col 57}{space 3}0.083{col 65}{space 4}-.3657113{col 78}{space 3} .0224468
{txt}{space 16}outofLF {c |}{col 25}{res}{space 2}-.1442088{col 37}{space 2} .0817634{col 48}{space 1}   -1.76{col 57}{space 3}0.078{col 65}{space 4}-.3044715{col 78}{space 3}  .016054
{txt}{space 13}goodhealth {c |}{col 25}{res}{space 2} .0121369{col 37}{space 2} .0135354{col 48}{space 1}    0.90{col 57}{space 3}0.370{col 65}{space 4}-.0143937{col 78}{space 3} .0386674
{txt}{space 18}white {c |}{col 25}{res}{space 2} .1818964{col 37}{space 2} .0607379{col 48}{space 1}    2.99{col 57}{space 3}0.003{col 65}{space 4} .0628452{col 78}{space 3} .3009475
{txt}{space 18}black {c |}{col 25}{res}{space 2} .2165308{col 37}{space 2} .0619945{col 48}{space 1}    3.49{col 57}{space 3}0.000{col 65}{space 4} .0950166{col 78}{space 3} .3380449
{txt}{space 17}latino {c |}{col 25}{res}{space 2} .2310626{col 37}{space 2} .0726508{col 48}{space 1}    3.18{col 57}{space 3}0.001{col 65}{space 4} .0886612{col 78}{space 3}  .373464
{txt}{space 18}asian {c |}{col 25}{res}{space 2} .2184793{col 37}{space 2} .0709328{col 48}{space 1}    3.08{col 57}{space 3}0.002{col 65}{space 4} .0794453{col 78}{space 3} .3575132
{txt}{space 12}whitecollar {c |}{col 25}{res}{space 2}-.0191288{col 37}{space 2} .0142438{col 48}{space 1}   -1.34{col 57}{space 3}0.179{col 65}{space 4}-.0470477{col 78}{space 3} .0087902
{txt}{space 13}bluecollar {c |}{col 25}{res}{space 2}-.0132008{col 37}{space 2} .0206195{col 48}{space 1}   -0.64{col 57}{space 3}0.522{col 65}{space 4}-.0536167{col 78}{space 3} .0272151
{txt}{space 10}serviceworker {c |}{col 25}{res}{space 2} .0065323{col 37}{space 2} .0129765{col 48}{space 1}    0.50{col 57}{space 3}0.615{col 65}{space 4}-.0189027{col 78}{space 3} .0319672
{txt}{space 12}mv_thirties {c |}{col 25}{res}{space 2}        0{col 37}{txt}  (omitted)
{space 12}mv_fourties {c |}{col 25}{res}{space 2}        0{col 37}{txt}  (omitted)
{space 13}mv_fifties {c |}{col 25}{res}{space 2}        0{col 37}{txt}  (omitted)
{space 13}mv_sixties {c |}{col 25}{res}{space 2}        0{col 37}{txt}  (omitted)
{space 11}mv_seventies {c |}{col 25}{res}{space 2}        0{col 37}{txt}  (omitted)
{space 5}mv_income2quartile {c |}{col 25}{res}{space 2}        0{col 37}{txt}  (omitted)
{space 5}mv_income3quartile {c |}{col 25}{res}{space 2}        0{col 37}{txt}  (omitted)
{space 5}mv_income4quartile {c |}{col 25}{res}{space 2}        0{col 37}{txt}  (omitted)
{space 6}mv_incomenoanswer {c |}{col 25}{res}{space 2} .1026816{col 37}{space 2} .0269281{col 48}{space 1}    3.81{col 57}{space 3}0.000{col 65}{space 4} .0499004{col 78}{space 3} .1554627
{txt}{space 14}mv_female {c |}{col 25}{res}{space 2}        0{col 37}{txt}  (omitted)
{space 10}mv_highschool {c |}{col 25}{res}{space 2}-.0024681{col 37}{space 2} .0226845{col 48}{space 1}   -0.11{col 57}{space 3}0.913{col 65}{space 4}-.0469315{col 78}{space 3} .0419952
{txt}{space 13}mv_college {c |}{col 25}{res}{space 2}        0{col 37}{txt}  (omitted)
{space 10}mv_noreligion {c |}{col 25}{res}{space 2}-.0337056{col 37}{space 2} .0390758{col 48}{space 1}   -0.86{col 57}{space 3}0.388{col 65}{space 4}-.1102972{col 78}{space 3} .0428859
{txt}mv_christiannotcatholic {c |}{col 25}{res}{space 2}        0{col 37}{txt}  (omitted)
{space 12}mv_catholic {c |}{col 25}{res}{space 2}        0{col 37}{txt}  (omitted)
{space 6}mv_fulltimeworker {c |}{col 25}{res}{space 2}        0{col 37}{txt}  (omitted)
{space 6}mv_parttimeworker {c |}{col 25}{res}{space 2}-.0962077{col 37}{space 2} .0769338{col 48}{space 1}   -1.25{col 57}{space 3}0.211{col 65}{space 4} -.247004{col 78}{space 3} .0545886
{txt}{space 10}mv_unemployed {c |}{col 25}{res}{space 2}        0{col 37}{txt}  (omitted)
{space 8}mv_selfemployed {c |}{col 25}{res}{space 2} .1193737{col 37}{space 2} .0788763{col 48}{space 1}    1.51{col 57}{space 3}0.130{col 65}{space 4}-.0352301{col 78}{space 3} .2739775
{txt}{space 13}mv_outofLF {c |}{col 25}{res}{space 2}        0{col 37}{txt}  (omitted)
{space 10}mv_goodhealth {c |}{col 25}{res}{space 2} .5024167{col 37}{space 2} .1407234{col 48}{space 1}    3.57{col 57}{space 3}0.000{col 65}{space 4} .2265876{col 78}{space 3} .7782459
{txt}{space 15}mv_white {c |}{col 25}{res}{space 2} .3573627{col 37}{space 2} .1062325{col 48}{space 1}    3.36{col 57}{space 3}0.001{col 65}{space 4} .1491384{col 78}{space 3} .5655869
{txt}{space 15}mv_black {c |}{col 25}{res}{space 2}        0{col 37}{txt}  (omitted)
{space 14}mv_latino {c |}{col 25}{res}{space 2}        0{col 37}{txt}  (omitted)
{space 15}mv_asian {c |}{col 25}{res}{space 2}        0{col 37}{txt}  (omitted)
{space 9}mv_whitecollar {c |}{col 25}{res}{space 2} .2201343{col 37}{space 2} .0789132{col 48}{space 1}    2.79{col 57}{space 3}0.005{col 65}{space 4} .0654581{col 78}{space 3} .3748106
{txt}{space 10}mv_bluecollar {c |}{col 25}{res}{space 2}        0{col 37}{txt}  (omitted)
{space 7}mv_serviceworker {c |}{col 25}{res}{space 2}        0{col 37}{txt}  (omitted)
{space 23} {c |}
{space 16}country {c |}
{space 15}Austria  {c |}{col 25}{res}{space 2} .3945586{col 37}{space 2} .1387946{col 48}{space 1}    2.84{col 57}{space 3}0.004{col 65}{space 4} .1225101{col 78}{space 3} .6666071
{txt}{space 16}France  {c |}{col 25}{res}{space 2}        0{col 37}{txt}  (omitted)
{space 15}Germany  {c |}{col 25}{res}{space 2} .4745848{col 37}{space 2} .1485947{col 48}{space 1}    3.19{col 57}{space 3}0.001{col 65}{space 4} .1833274{col 78}{space 3} .7658423
{txt}{space 17}Italy  {c |}{col 25}{res}{space 2}  .489016{col 37}{space 2} .1380549{col 48}{space 1}    3.54{col 57}{space 3}0.000{col 65}{space 4} .2184173{col 78}{space 3} .7596147
{txt}{space 11}New_Zealand  {c |}{col 25}{res}{space 2}        0{col 37}{txt}  (omitted)
{space 17}Spain  {c |}{col 25}{res}{space 2}        0{col 37}{txt}  (omitted)
{space 16}Sweden  {c |}{col 25}{res}{space 2} .1355698{col 37}{space 2} .0309267{col 48}{space 1}    4.38{col 57}{space 3}0.000{col 65}{space 4} .0749509{col 78}{space 3} .1961886
{txt}{space 20}UK  {c |}{col 25}{res}{space 2}        0{col 37}{txt}  (omitted)
{space 20}US  {c |}{col 25}{res}{space 2}        0{col 37}{txt}  (omitted)
{space 23} {c |}
{space 18}_cons {c |}{col 25}{res}{space 2} .1021811{col 37}{space 2} .0616249{col 48}{space 1}    1.66{col 57}{space 3}0.097{col 65}{space 4}-.0186086{col 78}{space 3} .2229707
{txt}{hline 24}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{help ivreg2##idtest:Underidentification test} (Kleibergen-Paap rk LM statistic):{res}{col 71}  28.470
{txt}{col 52}Chi-sq({res}15{txt}) P-val =  {res}{col 73}0.0188
{txt}{hline 78}
{help ivreg2##widtest:Weak identification test} (Cragg-Donald Wald F statistic):{res}{col 71}   1.861
{txt}                         (Kleibergen-Paap rk Wald F statistic):{res}{col 71}   1.903
{txt}Stock-Yogo weak ID test critical values:{res}{txt}{col 42} 5% maximal IV relative bias{res}{col 73} 21.23
{txt}{col 42}10% maximal IV relative bias{res}{col 73} 11.51
{txt}{col 42}20% maximal IV relative bias{res}{col 73}  6.42
{txt}{col 42}30% maximal IV relative bias{res}{col 73}  4.63
{txt}{col 42}10% maximal IV size{res}{col 73} 50.39
{txt}{col 42}15% maximal IV size{res}{col 73} 26.80
{txt}{col 42}20% maximal IV size{res}{col 73} 18.72
{txt}{col 42}25% maximal IV size{res}{col 73} 14.60
{txt}Source: Stock-Yogo (2005).  Reproduced by permission.
NB: Critical values are for Cragg-Donald F statistic and i.i.d. errors.
{hline 78}
{help ivreg2##overidtests:Hansen J statistic} (overidentification test of all instruments):{res}{col 71}  17.717
{txt}{col 52}Chi-sq({res}14{txt}) P-val =  {res}{col 73}0.2200
{txt}{hline 78}
Instrumented:{col 23}satis_head
Included instruments:{col 23}thirties fourties fifties sixties seventies
{col 23}income2quartile income3quartile income4quartile
{col 23}incomenoanswer female highschool college noreligion
{col 23}christiannotcatholic catholic fulltimeworker
{col 23}parttimeworker unemployed selfemployed outofLF goodhealth
{col 23}white black latino asian whitecollar bluecollar
{col 23}serviceworker mv_incomenoanswer mv_highschool
{col 23}mv_noreligion mv_parttimeworker mv_selfemployed
{col 23}mv_goodhealth mv_white mv_whitecollar 2.country 5.country
{col 23}6.country 10.country
Excluded instruments:{col 23}TH1_TE1 TH1_TE2 TH1_TE3 TH1_TE4 TH2_TE1 TH2_TE2 TH2_TE3
{col 23}TH2_TE4 TH3_TE1 TH3_TE2 TH3_TE3 TH3_TE4 TH4_TE1 TH4_TE2
{col 23}TH4_TE3
Dropped collinear:{col 23}mv_thirties mv_fourties mv_fifties mv_sixties
{col 23}mv_seventies mv_income2quartile mv_income3quartile
{col 23}mv_income4quartile mv_female mv_college
{col 23}mv_christiannotcatholic mv_catholic mv_fulltimeworker
{col 23}mv_unemployed mv_outofLF mv_black mv_latino mv_asian
{col 23}mv_bluecollar mv_serviceworker 4.country 7.country
{col 23}9.country 11.country 12.country
{hline 78}
({res}est5{txt} stored)

{com}. 
. estadd scalar fstat = e(widstat)

{txt}added scalar:
              e(fstat) =  {res}1.9025888
{txt}
{com}. estadd local CFE "X", replace

{txt}added macro:
                e(CFE) : "{res:X}"

{com}. estadd local IV "16 IVs", replace

{txt}added macro:
                 e(IV) : "{res:16 IVs}"

{com}. estadd local Controls "X", replace

{txt}added macro:
           e(Controls) : "{res:X}"

{com}. get_mean

{txt}added scalar:
                 e(av) =  {res}.169
{txt}
{com}. 
. /* E.10. Col 6 */
. eststo: ivreg2 army ///
>         (satis_head = treatc) $controls $mv_controls i.country, small robust 
{txt}Warning - collinearities detected
Vars dropped:{col 21}mv_thirties mv_fourties mv_fifties mv_sixties mv_seventies
{col 21}mv_income2quartile mv_income3quartile mv_income4quartile
{col 21}mv_female mv_college mv_christiannotcatholic mv_catholic
{col 21}mv_fulltimeworker mv_unemployed mv_outofLF mv_black
{col 21}mv_latino mv_asian mv_bluecollar mv_serviceworker 4.country
{col 21}7.country 9.country 11.country 12.country
{res}
{txt}IV (2SLS) estimation
{hline 20}

Estimates efficient for homoskedasticity only
Statistics robust to heteroskedasticity

{col 55}Number of obs = {res}   20534
{txt}{col 55}F( 41, 20492) = {res}   31.77
{txt}{col 55}Prob > F      = {res}  0.0000
{txt}Total (centered) SS     = {res} 2876.986461{txt}{col 55}Centered R2   = {res} -0.0489
{txt}Total (uncentered) SS   = {res}        3460{txt}{col 55}Uncentered R2 = {res}  0.1279
{txt}Residual SS             = {res} 3017.566035{txt}{col 55}Root MSE      = {res}   .3837

{txt}{hline 24}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 25}{c |}{col 37}    Robust
{col 1}                   army{col 25}{c |} Coefficient{col 37}  std. err.{col 49}      t{col 57}   P>|t|{col 65}     [95% con{col 78}f. interval]
{hline 24}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 13}satis_head {c |}{col 25}{res}{space 2}-.4029722{col 37}{space 2} .3861392{col 48}{space 1}   -1.04{col 57}{space 3}0.297{col 65}{space 4}-1.159836{col 78}{space 3} .3538915
{txt}{space 15}thirties {c |}{col 25}{res}{space 2}-.0014871{col 37}{space 2} .0158039{col 48}{space 1}   -0.09{col 57}{space 3}0.925{col 65}{space 4}-.0324641{col 78}{space 3} .0294898
{txt}{space 15}fourties {c |}{col 25}{res}{space 2}-.0744893{col 37}{space 2} .0214087{col 48}{space 1}   -3.48{col 57}{space 3}0.001{col 65}{space 4} -.116452{col 78}{space 3}-.0325266
{txt}{space 16}fifties {c |}{col 25}{res}{space 2}-.1443835{col 37}{space 2}  .020057{col 48}{space 1}   -7.20{col 57}{space 3}0.000{col 65}{space 4}-.1836968{col 78}{space 3}-.1050702
{txt}{space 16}sixties {c |}{col 25}{res}{space 2}-.1898417{col 37}{space 2}  .020035{col 48}{space 1}   -9.48{col 57}{space 3}0.000{col 65}{space 4}-.2291119{col 78}{space 3}-.1505715
{txt}{space 14}seventies {c |}{col 25}{res}{space 2}-.1860978{col 37}{space 2} .0146509{col 48}{space 1}  -12.70{col 57}{space 3}0.000{col 65}{space 4}-.2148147{col 78}{space 3}-.1573809
{txt}{space 8}income2quartile {c |}{col 25}{res}{space 2}-.0188337{col 37}{space 2}  .010572{col 48}{space 1}   -1.78{col 57}{space 3}0.075{col 65}{space 4}-.0395557{col 78}{space 3} .0018883
{txt}{space 8}income3quartile {c |}{col 25}{res}{space 2}-.0163564{col 37}{space 2} .0130017{col 48}{space 1}   -1.26{col 57}{space 3}0.208{col 65}{space 4}-.0418408{col 78}{space 3} .0091279
{txt}{space 8}income4quartile {c |}{col 25}{res}{space 2}-.0261782{col 37}{space 2} .0194939{col 48}{space 1}   -1.34{col 57}{space 3}0.179{col 65}{space 4}-.0643878{col 78}{space 3} .0120314
{txt}{space 9}incomenoanswer {c |}{col 25}{res}{space 2}-.0141796{col 37}{space 2} .0130463{col 48}{space 1}   -1.09{col 57}{space 3}0.277{col 65}{space 4}-.0397515{col 78}{space 3} .0113922
{txt}{space 17}female {c |}{col 25}{res}{space 2} .0032498{col 37}{space 2} .0073858{col 48}{space 1}    0.44{col 57}{space 3}0.660{col 65}{space 4} -.011227{col 78}{space 3} .0177265
{txt}{space 13}highschool {c |}{col 25}{res}{space 2}-.0558328{col 37}{space 2} .0097235{col 48}{space 1}   -5.74{col 57}{space 3}0.000{col 65}{space 4}-.0748916{col 78}{space 3} -.036774
{txt}{space 16}college {c |}{col 25}{res}{space 2}-.0955577{col 37}{space 2} .0090816{col 48}{space 1}  -10.52{col 57}{space 3}0.000{col 65}{space 4}-.1133584{col 78}{space 3} -.077757
{txt}{space 13}noreligion {c |}{col 25}{res}{space 2}-.1227611{col 37}{space 2} .0164594{col 48}{space 1}   -7.46{col 57}{space 3}0.000{col 65}{space 4}-.1550229{col 78}{space 3}-.0904993
{txt}{space 3}christiannotcatholic {c |}{col 25}{res}{space 2} -.039435{col 37}{space 2} .0273564{col 48}{space 1}   -1.44{col 57}{space 3}0.149{col 65}{space 4}-.0930556{col 78}{space 3} .0141857
{txt}{space 15}catholic {c |}{col 25}{res}{space 2}-.0272127{col 37}{space 2} .0168417{col 48}{space 1}   -1.62{col 57}{space 3}0.106{col 65}{space 4}-.0602237{col 78}{space 3} .0057984
{txt}{space 9}fulltimeworker {c |}{col 25}{res}{space 2}-.1472729{col 37}{space 2} .1221085{col 48}{space 1}   -1.21{col 57}{space 3}0.228{col 65}{space 4}-.3866153{col 78}{space 3} .0920695
{txt}{space 9}parttimeworker {c |}{col 25}{res}{space 2}-.1285539{col 37}{space 2} .1237735{col 48}{space 1}   -1.04{col 57}{space 3}0.299{col 65}{space 4}-.3711598{col 78}{space 3}  .114052
{txt}{space 13}unemployed {c |}{col 25}{res}{space 2}-.1867431{col 37}{space 2} .1355821{col 48}{space 1}   -1.38{col 57}{space 3}0.168{col 65}{space 4}-.4524949{col 78}{space 3} .0790087
{txt}{space 11}selfemployed {c |}{col 25}{res}{space 2}-.1744447{col 37}{space 2} .1484003{col 48}{space 1}   -1.18{col 57}{space 3}0.240{col 65}{space 4} -.465321{col 78}{space 3} .1164317
{txt}{space 16}outofLF {c |}{col 25}{res}{space 2}   -.1467{col 37}{space 2} .1272471{col 48}{space 1}   -1.15{col 57}{space 3}0.249{col 65}{space 4}-.3961146{col 78}{space 3} .1027145
{txt}{space 13}goodhealth {c |}{col 25}{res}{space 2} .0125158{col 37}{space 2} .0201032{col 48}{space 1}    0.62{col 57}{space 3}0.534{col 65}{space 4} -.026888{col 78}{space 3} .0519197
{txt}{space 18}white {c |}{col 25}{res}{space 2} .1829046{col 37}{space 2} .0719808{col 48}{space 1}    2.54{col 57}{space 3}0.011{col 65}{space 4} .0418164{col 78}{space 3} .3239928
{txt}{space 18}black {c |}{col 25}{res}{space 2} .2166316{col 37}{space 2} .0622507{col 48}{space 1}    3.48{col 57}{space 3}0.001{col 65}{space 4} .0946153{col 78}{space 3}  .338648
{txt}{space 17}latino {c |}{col 25}{res}{space 2} .2313207{col 37}{space 2} .0734292{col 48}{space 1}    3.15{col 57}{space 3}0.002{col 65}{space 4} .0873936{col 78}{space 3} .3752479
{txt}{space 18}asian {c |}{col 25}{res}{space 2} .2190151{col 37}{space 2} .0737327{col 48}{space 1}    2.97{col 57}{space 3}0.003{col 65}{space 4} .0744931{col 78}{space 3} .3635372
{txt}{space 12}whitecollar {c |}{col 25}{res}{space 2} -.019275{col 37}{space 2}  .015366{col 48}{space 1}   -1.25{col 57}{space 3}0.210{col 65}{space 4}-.0493935{col 78}{space 3} .0108435
{txt}{space 13}bluecollar {c |}{col 25}{res}{space 2}-.0136158{col 37}{space 2} .0260731{col 48}{space 1}   -0.52{col 57}{space 3}0.602{col 65}{space 4}-.0647212{col 78}{space 3} .0374895
{txt}{space 10}serviceworker {c |}{col 25}{res}{space 2} .0063647{col 37}{space 2} .0145161{col 48}{space 1}    0.44{col 57}{space 3}0.661{col 65}{space 4} -.022088{col 78}{space 3} .0348174
{txt}{space 12}mv_thirties {c |}{col 25}{res}{space 2}        0{col 37}{txt}  (omitted)
{space 12}mv_fourties {c |}{col 25}{res}{space 2}        0{col 37}{txt}  (omitted)
{space 13}mv_fifties {c |}{col 25}{res}{space 2}        0{col 37}{txt}  (omitted)
{space 13}mv_sixties {c |}{col 25}{res}{space 2}        0{col 37}{txt}  (omitted)
{space 11}mv_seventies {c |}{col 25}{res}{space 2}        0{col 37}{txt}  (omitted)
{space 5}mv_income2quartile {c |}{col 25}{res}{space 2}        0{col 37}{txt}  (omitted)
{space 5}mv_income3quartile {c |}{col 25}{res}{space 2}        0{col 37}{txt}  (omitted)
{space 5}mv_income4quartile {c |}{col 25}{res}{space 2}        0{col 37}{txt}  (omitted)
{space 6}mv_incomenoanswer {c |}{col 25}{res}{space 2} .1021187{col 37}{space 2} .0347012{col 48}{space 1}    2.94{col 57}{space 3}0.003{col 65}{space 4} .0341015{col 78}{space 3} .1701359
{txt}{space 14}mv_female {c |}{col 25}{res}{space 2}        0{col 37}{txt}  (omitted)
{space 10}mv_highschool {c |}{col 25}{res}{space 2}-.0025423{col 37}{space 2} .0228936{col 48}{space 1}   -0.11{col 57}{space 3}0.912{col 65}{space 4}-.0474156{col 78}{space 3}  .042331
{txt}{space 13}mv_college {c |}{col 25}{res}{space 2}        0{col 37}{txt}  (omitted)
{space 10}mv_noreligion {c |}{col 25}{res}{space 2}-.0336269{col 37}{space 2} .0392851{col 48}{space 1}   -0.86{col 57}{space 3}0.392{col 65}{space 4}-.1106289{col 78}{space 3} .0433751
{txt}mv_christiannotcatholic {c |}{col 25}{res}{space 2}        0{col 37}{txt}  (omitted)
{space 12}mv_catholic {c |}{col 25}{res}{space 2}        0{col 37}{txt}  (omitted)
{space 6}mv_fulltimeworker {c |}{col 25}{res}{space 2}        0{col 37}{txt}  (omitted)
{space 6}mv_parttimeworker {c |}{col 25}{res}{space 2}-.0984178{col 37}{space 2} .1155747{col 48}{space 1}   -0.85{col 57}{space 3}0.394{col 65}{space 4}-.3249534{col 78}{space 3} .1281179
{txt}{space 10}mv_unemployed {c |}{col 25}{res}{space 2}        0{col 37}{txt}  (omitted)
{space 8}mv_selfemployed {c |}{col 25}{res}{space 2} .1216383{col 37}{space 2}  .118462{col 48}{space 1}    1.03{col 57}{space 3}0.305{col 65}{space 4}-.1105566{col 78}{space 3} .3538332
{txt}{space 13}mv_outofLF {c |}{col 25}{res}{space 2}        0{col 37}{txt}  (omitted)
{space 10}mv_goodhealth {c |}{col 25}{res}{space 2} .5038425{col 37}{space 2} .1517567{col 48}{space 1}    3.32{col 57}{space 3}0.001{col 65}{space 4} .2063872{col 78}{space 3} .8012977
{txt}{space 15}mv_white {c |}{col 25}{res}{space 2} .3602495{col 37}{space 2} .1544223{col 48}{space 1}    2.33{col 57}{space 3}0.020{col 65}{space 4} .0575694{col 78}{space 3} .6629296
{txt}{space 15}mv_black {c |}{col 25}{res}{space 2}        0{col 37}{txt}  (omitted)
{space 14}mv_latino {c |}{col 25}{res}{space 2}        0{col 37}{txt}  (omitted)
{space 15}mv_asian {c |}{col 25}{res}{space 2}        0{col 37}{txt}  (omitted)
{space 9}mv_whitecollar {c |}{col 25}{res}{space 2} .2223888{col 37}{space 2} .1185927{col 48}{space 1}    1.88{col 57}{space 3}0.061{col 65}{space 4}-.0100624{col 78}{space 3}   .45484
{txt}{space 10}mv_bluecollar {c |}{col 25}{res}{space 2}        0{col 37}{txt}  (omitted)
{space 7}mv_serviceworker {c |}{col 25}{res}{space 2}        0{col 37}{txt}  (omitted)
{space 23} {c |}
{space 16}country {c |}
{space 15}Austria  {c |}{col 25}{res}{space 2}  .398468{col 37}{space 2} .2060136{col 48}{space 1}    1.93{col 57}{space 3}0.053{col 65}{space 4}-.0053352{col 78}{space 3} .8022712
{txt}{space 16}France  {c |}{col 25}{res}{space 2}        0{col 37}{txt}  (omitted)
{space 15}Germany  {c |}{col 25}{res}{space 2} .4788199{col 37}{space 2} .2221728{col 48}{space 1}    2.16{col 57}{space 3}0.031{col 65}{space 4} .0433434{col 78}{space 3} .9142964
{txt}{space 17}Italy  {c |}{col 25}{res}{space 2} .4927247{col 37}{space 2} .1994649{col 48}{space 1}    2.47{col 57}{space 3}0.014{col 65}{space 4} .1017577{col 78}{space 3} .8836917
{txt}{space 11}New_Zealand  {c |}{col 25}{res}{space 2}        0{col 37}{txt}  (omitted)
{space 17}Spain  {c |}{col 25}{res}{space 2}        0{col 37}{txt}  (omitted)
{space 16}Sweden  {c |}{col 25}{res}{space 2} .1360009{col 37}{space 2} .0354605{col 48}{space 1}    3.84{col 57}{space 3}0.000{col 65}{space 4} .0664955{col 78}{space 3} .2055062
{txt}{space 20}UK  {c |}{col 25}{res}{space 2}        0{col 37}{txt}  (omitted)
{space 20}US  {c |}{col 25}{res}{space 2}        0{col 37}{txt}  (omitted)
{space 23} {c |}
{space 18}_cons {c |}{col 25}{res}{space 2} .1022128{col 37}{space 2}  .061811{col 48}{space 1}    1.65{col 57}{space 3}0.098{col 65}{space 4}-.0189417{col 78}{space 3} .2233674
{txt}{hline 24}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{help ivreg2##idtest:Underidentification test} (Kleibergen-Paap rk LM statistic):{res}{col 71}  11.720
{txt}{col 52}Chi-sq({res}1{txt}) P-val =  {res}{col 73}0.0006
{txt}{hline 78}
{help ivreg2##widtest:Weak identification test} (Cragg-Donald Wald F statistic):{res}{col 71}  11.667
{txt}                         (Kleibergen-Paap rk Wald F statistic):{res}{col 71}  11.720
{txt}Stock-Yogo weak ID test critical values:{res}{txt}{col 42}10% maximal IV size{res}{col 73} 16.38
{txt}{col 42}15% maximal IV size{res}{col 73}  8.96
{txt}{col 42}20% maximal IV size{res}{col 73}  6.66
{txt}{col 42}25% maximal IV size{res}{col 73}  5.53
{txt}Source: Stock-Yogo (2005).  Reproduced by permission.
NB: Critical values are for Cragg-Donald F statistic and i.i.d. errors.
{hline 78}
{help ivreg2##overidtests:Hansen J statistic} (overidentification test of all instruments):{res}{col 71}   0.000
{txt}{col 50}(equation exactly identified)
{hline 78}
Instrumented:{col 23}satis_head
Included instruments:{col 23}thirties fourties fifties sixties seventies
{col 23}income2quartile income3quartile income4quartile
{col 23}incomenoanswer female highschool college noreligion
{col 23}christiannotcatholic catholic fulltimeworker
{col 23}parttimeworker unemployed selfemployed outofLF goodhealth
{col 23}white black latino asian whitecollar bluecollar
{col 23}serviceworker mv_incomenoanswer mv_highschool
{col 23}mv_noreligion mv_parttimeworker mv_selfemployed
{col 23}mv_goodhealth mv_white mv_whitecollar 2.country 5.country
{col 23}6.country 10.country
Excluded instruments:{col 23}treatc
Dropped collinear:{col 23}mv_thirties mv_fourties mv_fifties mv_sixties
{col 23}mv_seventies mv_income2quartile mv_income3quartile
{col 23}mv_income4quartile mv_female mv_college
{col 23}mv_christiannotcatholic mv_catholic mv_fulltimeworker
{col 23}mv_unemployed mv_outofLF mv_black mv_latino mv_asian
{col 23}mv_bluecollar mv_serviceworker 4.country 7.country
{col 23}9.country 11.country 12.country
{hline 78}
({res}est6{txt} stored)

{com}. 
. estadd scalar fstat = e(widstat)

{txt}added scalar:
              e(fstat) =  {res}11.719873
{txt}
{com}. estadd local CFE "X", replace

{txt}added macro:
                e(CFE) : "{res:X}"

{com}. estadd local IV "SumIV", replace

{txt}added macro:
                 e(IV) : "{res:SumIV}"

{com}. estadd local Controls "X", replace

{txt}added macro:
           e(Controls) : "{res:X}"

{com}. get_mean

{txt}added scalar:
                 e(av) =  {res}.169
{txt}
{com}. 
. /* E.10. Col 7 */
. eststo: ivreg2 democ ///
>         (satis_head = TH1_TE1 TH1_TE2 TH1_TE3 TH1_TE4 TH2_TE1 TH2_TE2 TH2_TE3 TH2_TE4 TH3_TE1 TH3_TE2 TH3_TE3 TH3_TE4 TH4_TE1 TH4_TE2 TH4_TE3) $controls $mv_controls i.country, small robust 
{txt}Warning - collinearities detected
Vars dropped:{col 21}mv_thirties mv_fourties mv_fifties mv_sixties mv_seventies
{col 21}mv_income2quartile mv_income3quartile mv_income4quartile
{col 21}mv_female mv_college mv_christiannotcatholic mv_catholic
{col 21}mv_fulltimeworker mv_unemployed mv_outofLF mv_black
{col 21}mv_latino mv_asian mv_bluecollar mv_serviceworker 4.country
{col 21}7.country 9.country 11.country 12.country
{res}
{txt}IV (2SLS) estimation
{hline 20}

Estimates efficient for homoskedasticity only
Statistics robust to heteroskedasticity

{col 55}Number of obs = {res}   20536
{txt}{col 55}F( 41, 20494) = {res}   17.30
{txt}{col 55}Prob > F      = {res}  0.0000
{txt}Total (centered) SS     = {res} 1729.913469{txt}{col 55}Centered R2   = {res}  0.0441
{txt}Total (uncentered) SS   = {res}       18629{txt}{col 55}Uncentered R2 = {res}  0.9112
{txt}Residual SS             = {res} 1653.541772{txt}{col 55}Root MSE      = {res}    .284

{txt}{hline 24}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 25}{c |}{col 37}    Robust
{col 1}                  democ{col 25}{c |} Coefficient{col 37}  std. err.{col 49}      t{col 57}   P>|t|{col 65}     [95% con{col 78}f. interval]
{hline 24}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 13}satis_head {c |}{col 25}{res}{space 2} .0426625{col 37}{space 2} .1887566{col 48}{space 1}    0.23{col 57}{space 3}0.821{col 65}{space 4}-.3273155{col 78}{space 3} .4126405
{txt}{space 15}thirties {c |}{col 25}{res}{space 2} .0131321{col 37}{space 2} .0098507{col 48}{space 1}    1.33{col 57}{space 3}0.183{col 65}{space 4}-.0061761{col 78}{space 3} .0324402
{txt}{space 15}fourties {c |}{col 25}{res}{space 2} .0205045{col 37}{space 2} .0119594{col 48}{space 1}    1.71{col 57}{space 3}0.086{col 65}{space 4}-.0029369{col 78}{space 3} .0439459
{txt}{space 16}fifties {c |}{col 25}{res}{space 2}  .051282{col 37}{space 2} .0114651{col 48}{space 1}    4.47{col 57}{space 3}0.000{col 65}{space 4} .0288095{col 78}{space 3} .0737544
{txt}{space 16}sixties {c |}{col 25}{res}{space 2}  .072702{col 37}{space 2} .0113113{col 48}{space 1}    6.43{col 57}{space 3}0.000{col 65}{space 4} .0505308{col 78}{space 3} .0948731
{txt}{space 14}seventies {c |}{col 25}{res}{space 2} .0901445{col 37}{space 2} .0093549{col 48}{space 1}    9.64{col 57}{space 3}0.000{col 65}{space 4} .0718081{col 78}{space 3} .1084808
{txt}{space 8}income2quartile {c |}{col 25}{res}{space 2} .0286994{col 37}{space 2} .0071641{col 48}{space 1}    4.01{col 57}{space 3}0.000{col 65}{space 4} .0146573{col 78}{space 3} .0427416
{txt}{space 8}income3quartile {c |}{col 25}{res}{space 2} .0457148{col 37}{space 2} .0078617{col 48}{space 1}    5.81{col 57}{space 3}0.000{col 65}{space 4} .0303052{col 78}{space 3} .0611243
{txt}{space 8}income4quartile {c |}{col 25}{res}{space 2} .0516862{col 37}{space 2} .0104035{col 48}{space 1}    4.97{col 57}{space 3}0.000{col 65}{space 4} .0312945{col 78}{space 3}  .072078
{txt}{space 9}incomenoanswer {c |}{col 25}{res}{space 2} .0125875{col 37}{space 2} .0098662{col 48}{space 1}    1.28{col 57}{space 3}0.202{col 65}{space 4} -.006751{col 78}{space 3}  .031926
{txt}{space 17}female {c |}{col 25}{res}{space 2}-.0113402{col 37}{space 2} .0047104{col 48}{space 1}   -2.41{col 57}{space 3}0.016{col 65}{space 4} -.020573{col 78}{space 3}-.0021074
{txt}{space 13}highschool {c |}{col 25}{res}{space 2} .0271501{col 37}{space 2} .0074463{col 48}{space 1}    3.65{col 57}{space 3}0.000{col 65}{space 4} .0125548{col 78}{space 3} .0417454
{txt}{space 16}college {c |}{col 25}{res}{space 2} .0664271{col 37}{space 2} .0068139{col 48}{space 1}    9.75{col 57}{space 3}0.000{col 65}{space 4} .0530714{col 78}{space 3} .0797828
{txt}{space 13}noreligion {c |}{col 25}{res}{space 2} .0357047{col 37}{space 2} .0110658{col 48}{space 1}    3.23{col 57}{space 3}0.001{col 65}{space 4} .0140149{col 78}{space 3} .0573945
{txt}{space 3}christiannotcatholic {c |}{col 25}{res}{space 2} .0319748{col 37}{space 2} .0158831{col 48}{space 1}    2.01{col 57}{space 3}0.044{col 65}{space 4} .0008427{col 78}{space 3} .0631069
{txt}{space 15}catholic {c |}{col 25}{res}{space 2}  .033668{col 37}{space 2} .0112742{col 48}{space 1}    2.99{col 57}{space 3}0.003{col 65}{space 4} .0115696{col 78}{space 3} .0557663
{txt}{space 9}fulltimeworker {c |}{col 25}{res}{space 2}-.0005051{col 37}{space 2} .0599486{col 48}{space 1}   -0.01{col 57}{space 3}0.993{col 65}{space 4}-.1180091{col 78}{space 3} .1169988
{txt}{space 9}parttimeworker {c |}{col 25}{res}{space 2} .0261362{col 37}{space 2} .0621419{col 48}{space 1}    0.42{col 57}{space 3}0.674{col 65}{space 4}-.0956669{col 78}{space 3} .1479392
{txt}{space 13}unemployed {c |}{col 25}{res}{space 2}-.0066245{col 37}{space 2} .0678777{col 48}{space 1}   -0.10{col 57}{space 3}0.922{col 65}{space 4}-.1396702{col 78}{space 3} .1264212
{txt}{space 11}selfemployed {c |}{col 25}{res}{space 2}-.0071393{col 37}{space 2} .0752307{col 48}{space 1}   -0.09{col 57}{space 3}0.924{col 65}{space 4}-.1545975{col 78}{space 3} .1403189
{txt}{space 16}outofLF {c |}{col 25}{res}{space 2} .0117444{col 37}{space 2} .0627658{col 48}{space 1}    0.19{col 57}{space 3}0.852{col 65}{space 4}-.1112815{col 78}{space 3} .1347704
{txt}{space 13}goodhealth {c |}{col 25}{res}{space 2} .0319206{col 37}{space 2} .0104756{col 48}{space 1}    3.05{col 57}{space 3}0.002{col 65}{space 4} .0113875{col 78}{space 3} .0524536
{txt}{space 18}white {c |}{col 25}{res}{space 2} .0567206{col 37}{space 2} .0633201{col 48}{space 1}    0.90{col 57}{space 3}0.370{col 65}{space 4}-.0673919{col 78}{space 3}  .180833
{txt}{space 18}black {c |}{col 25}{res}{space 2} .0688025{col 37}{space 2}  .063555{col 48}{space 1}    1.08{col 57}{space 3}0.279{col 65}{space 4}-.0557704{col 78}{space 3} .1933754
{txt}{space 17}latino {c |}{col 25}{res}{space 2} .0985912{col 37}{space 2} .0679244{col 48}{space 1}    1.45{col 57}{space 3}0.147{col 65}{space 4}-.0345461{col 78}{space 3} .2317285
{txt}{space 18}asian {c |}{col 25}{res}{space 2} .0858817{col 37}{space 2} .0679568{col 48}{space 1}    1.26{col 57}{space 3}0.206{col 65}{space 4}-.0473191{col 78}{space 3} .2190825
{txt}{space 12}whitecollar {c |}{col 25}{res}{space 2}-.0103767{col 37}{space 2} .0108439{col 48}{space 1}   -0.96{col 57}{space 3}0.339{col 65}{space 4}-.0316316{col 78}{space 3} .0108781
{txt}{space 13}bluecollar {c |}{col 25}{res}{space 2}  -.02095{col 37}{space 2} .0160289{col 48}{space 1}   -1.31{col 57}{space 3}0.191{col 65}{space 4}-.0523679{col 78}{space 3} .0104678
{txt}{space 10}serviceworker {c |}{col 25}{res}{space 2}-.0123071{col 37}{space 2} .0099818{col 48}{space 1}   -1.23{col 57}{space 3}0.218{col 65}{space 4}-.0318721{col 78}{space 3}  .007258
{txt}{space 12}mv_thirties {c |}{col 25}{res}{space 2}        0{col 37}{txt}  (omitted)
{space 12}mv_fourties {c |}{col 25}{res}{space 2}        0{col 37}{txt}  (omitted)
{space 13}mv_fifties {c |}{col 25}{res}{space 2}        0{col 37}{txt}  (omitted)
{space 13}mv_sixties {c |}{col 25}{res}{space 2}        0{col 37}{txt}  (omitted)
{space 11}mv_seventies {c |}{col 25}{res}{space 2}        0{col 37}{txt}  (omitted)
{space 5}mv_income2quartile {c |}{col 25}{res}{space 2}        0{col 37}{txt}  (omitted)
{space 5}mv_income3quartile {c |}{col 25}{res}{space 2}        0{col 37}{txt}  (omitted)
{space 5}mv_income4quartile {c |}{col 25}{res}{space 2}        0{col 37}{txt}  (omitted)
{space 6}mv_incomenoanswer {c |}{col 25}{res}{space 2}-.0029005{col 37}{space 2} .0186807{col 48}{space 1}   -0.16{col 57}{space 3}0.877{col 65}{space 4}-.0395162{col 78}{space 3} .0337153
{txt}{space 14}mv_female {c |}{col 25}{res}{space 2}        0{col 37}{txt}  (omitted)
{space 10}mv_highschool {c |}{col 25}{res}{space 2} .0241489{col 37}{space 2} .0175701{col 48}{space 1}    1.37{col 57}{space 3}0.169{col 65}{space 4}-.0102899{col 78}{space 3} .0585877
{txt}{space 13}mv_college {c |}{col 25}{res}{space 2}        0{col 37}{txt}  (omitted)
{space 10}mv_noreligion {c |}{col 25}{res}{space 2} .0274792{col 37}{space 2} .0268362{col 48}{space 1}    1.02{col 57}{space 3}0.306{col 65}{space 4}-.0251219{col 78}{space 3} .0800803
{txt}mv_christiannotcatholic {c |}{col 25}{res}{space 2}        0{col 37}{txt}  (omitted)
{space 12}mv_catholic {c |}{col 25}{res}{space 2}        0{col 37}{txt}  (omitted)
{space 6}mv_fulltimeworker {c |}{col 25}{res}{space 2}        0{col 37}{txt}  (omitted)
{space 6}mv_parttimeworker {c |}{col 25}{res}{space 2} .0237084{col 37}{space 2} .0588964{col 48}{space 1}    0.40{col 57}{space 3}0.687{col 65}{space 4}-.0917333{col 78}{space 3} .1391501
{txt}{space 10}mv_unemployed {c |}{col 25}{res}{space 2}        0{col 37}{txt}  (omitted)
{space 8}mv_selfemployed {c |}{col 25}{res}{space 2}-.0491616{col 37}{space 2} .0602484{col 48}{space 1}   -0.82{col 57}{space 3}0.415{col 65}{space 4}-.1672533{col 78}{space 3} .0689301
{txt}{space 13}mv_outofLF {c |}{col 25}{res}{space 2}        0{col 37}{txt}  (omitted)
{space 10}mv_goodhealth {c |}{col 25}{res}{space 2}-.2106283{col 37}{space 2} .1462731{col 48}{space 1}   -1.44{col 57}{space 3}0.150{col 65}{space 4}-.4973352{col 78}{space 3} .0760786
{txt}{space 15}mv_white {c |}{col 25}{res}{space 2} .1501481{col 37}{space 2} .0925017{col 48}{space 1}    1.62{col 57}{space 3}0.105{col 65}{space 4}-.0311627{col 78}{space 3} .3314588
{txt}{space 15}mv_black {c |}{col 25}{res}{space 2}        0{col 37}{txt}  (omitted)
{space 14}mv_latino {c |}{col 25}{res}{space 2}        0{col 37}{txt}  (omitted)
{space 15}mv_asian {c |}{col 25}{res}{space 2}        0{col 37}{txt}  (omitted)
{space 9}mv_whitecollar {c |}{col 25}{res}{space 2}-.0523185{col 37}{space 2} .0597831{col 48}{space 1}   -0.88{col 57}{space 3}0.382{col 65}{space 4} -.169498{col 78}{space 3} .0648611
{txt}{space 10}mv_bluecollar {c |}{col 25}{res}{space 2}        0{col 37}{txt}  (omitted)
{space 7}mv_serviceworker {c |}{col 25}{res}{space 2}        0{col 37}{txt}  (omitted)
{space 23} {c |}
{space 16}country {c |}
{space 15}Austria  {c |}{col 25}{res}{space 2} .1497798{col 37}{space 2} .1152557{col 48}{space 1}    1.30{col 57}{space 3}0.194{col 65}{space 4}-.0761305{col 78}{space 3} .3756901
{txt}{space 16}France  {c |}{col 25}{res}{space 2}        0{col 37}{txt}  (omitted)
{space 15}Germany  {c |}{col 25}{res}{space 2} .0929704{col 37}{space 2} .1220357{col 48}{space 1}    0.76{col 57}{space 3}0.446{col 65}{space 4}-.1462293{col 78}{space 3}   .33217
{txt}{space 17}Italy  {c |}{col 25}{res}{space 2} .1344663{col 37}{space 2} .1137166{col 48}{space 1}    1.18{col 57}{space 3}0.237{col 65}{space 4}-.0884274{col 78}{space 3} .3573599
{txt}{space 11}New_Zealand  {c |}{col 25}{res}{space 2}        0{col 37}{txt}  (omitted)
{space 17}Spain  {c |}{col 25}{res}{space 2}        0{col 37}{txt}  (omitted)
{space 16}Sweden  {c |}{col 25}{res}{space 2}-.0629729{col 37}{space 2} .0232687{col 48}{space 1}   -2.71{col 57}{space 3}0.007{col 65}{space 4}-.1085814{col 78}{space 3}-.0173644
{txt}{space 20}UK  {c |}{col 25}{res}{space 2}        0{col 37}{txt}  (omitted)
{space 20}US  {c |}{col 25}{res}{space 2}        0{col 37}{txt}  (omitted)
{space 23} {c |}
{space 18}_cons {c |}{col 25}{res}{space 2} .6585216{col 37}{space 2} .0627885{col 48}{space 1}   10.49{col 57}{space 3}0.000{col 65}{space 4} .5354512{col 78}{space 3}  .781592
{txt}{hline 24}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{help ivreg2##idtest:Underidentification test} (Kleibergen-Paap rk LM statistic):{res}{col 71}  28.715
{txt}{col 52}Chi-sq({res}15{txt}) P-val =  {res}{col 73}0.0175
{txt}{hline 78}
{help ivreg2##widtest:Weak identification test} (Cragg-Donald Wald F statistic):{res}{col 71}   1.879
{txt}                         (Kleibergen-Paap rk Wald F statistic):{res}{col 71}   1.919
{txt}Stock-Yogo weak ID test critical values:{res}{txt}{col 42} 5% maximal IV relative bias{res}{col 73} 21.23
{txt}{col 42}10% maximal IV relative bias{res}{col 73} 11.51
{txt}{col 42}20% maximal IV relative bias{res}{col 73}  6.42
{txt}{col 42}30% maximal IV relative bias{res}{col 73}  4.63
{txt}{col 42}10% maximal IV size{res}{col 73} 50.39
{txt}{col 42}15% maximal IV size{res}{col 73} 26.80
{txt}{col 42}20% maximal IV size{res}{col 73} 18.72
{txt}{col 42}25% maximal IV size{res}{col 73} 14.60
{txt}Source: Stock-Yogo (2005).  Reproduced by permission.
NB: Critical values are for Cragg-Donald F statistic and i.i.d. errors.
{hline 78}
{help ivreg2##overidtests:Hansen J statistic} (overidentification test of all instruments):{res}{col 71}  11.147
{txt}{col 52}Chi-sq({res}14{txt}) P-val =  {res}{col 73}0.6744
{txt}{hline 78}
Instrumented:{col 23}satis_head
Included instruments:{col 23}thirties fourties fifties sixties seventies
{col 23}income2quartile income3quartile income4quartile
{col 23}incomenoanswer female highschool college noreligion
{col 23}christiannotcatholic catholic fulltimeworker
{col 23}parttimeworker unemployed selfemployed outofLF goodhealth
{col 23}white black latino asian whitecollar bluecollar
{col 23}serviceworker mv_incomenoanswer mv_highschool
{col 23}mv_noreligion mv_parttimeworker mv_selfemployed
{col 23}mv_goodhealth mv_white mv_whitecollar 2.country 5.country
{col 23}6.country 10.country
Excluded instruments:{col 23}TH1_TE1 TH1_TE2 TH1_TE3 TH1_TE4 TH2_TE1 TH2_TE2 TH2_TE3
{col 23}TH2_TE4 TH3_TE1 TH3_TE2 TH3_TE3 TH3_TE4 TH4_TE1 TH4_TE2
{col 23}TH4_TE3
Dropped collinear:{col 23}mv_thirties mv_fourties mv_fifties mv_sixties
{col 23}mv_seventies mv_income2quartile mv_income3quartile
{col 23}mv_income4quartile mv_female mv_college
{col 23}mv_christiannotcatholic mv_catholic mv_fulltimeworker
{col 23}mv_unemployed mv_outofLF mv_black mv_latino mv_asian
{col 23}mv_bluecollar mv_serviceworker 4.country 7.country
{col 23}9.country 11.country 12.country
{hline 78}
({res}est7{txt} stored)

{com}. 
. estadd scalar fstat = e(widstat)

{txt}added scalar:
              e(fstat) =  {res}1.9190373
{txt}
{com}. estadd local IV "16 IVs", replace

{txt}added macro:
                 e(IV) : "{res:16 IVs}"

{com}. estadd local CFE "X", replace

{txt}added macro:
                e(CFE) : "{res:X}"

{com}. estadd local Controls "X", replace

{txt}added macro:
           e(Controls) : "{res:X}"

{com}. get_mean

{txt}added scalar:
                 e(av) =  {res}.907
{txt}
{com}. 
. /* E.10. Col 8 */
. eststo: ivreg2 democ ///
>         (satis_head = treatc ) $controls $mv_controls i.country, small robust 
{txt}Warning - collinearities detected
Vars dropped:{col 21}mv_thirties mv_fourties mv_fifties mv_sixties mv_seventies
{col 21}mv_income2quartile mv_income3quartile mv_income4quartile
{col 21}mv_female mv_college mv_christiannotcatholic mv_catholic
{col 21}mv_fulltimeworker mv_unemployed mv_outofLF mv_black
{col 21}mv_latino mv_asian mv_bluecollar mv_serviceworker 4.country
{col 21}7.country 9.country 11.country 12.country
{res}
{txt}IV (2SLS) estimation
{hline 20}

Estimates efficient for homoskedasticity only
Statistics robust to heteroskedasticity

{col 55}Number of obs = {res}   20536
{txt}{col 55}F( 41, 20494) = {res}   17.15
{txt}{col 55}Prob > F      = {res}  0.0000
{txt}Total (centered) SS     = {res} 1729.913469{txt}{col 55}Centered R2   = {res}  0.0406
{txt}Total (uncentered) SS   = {res}       18629{txt}{col 55}Uncentered R2 = {res}  0.9109
{txt}Residual SS             = {res} 1659.705027{txt}{col 55}Root MSE      = {res}   .2846

{txt}{hline 24}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 25}{c |}{col 37}    Robust
{col 1}                  democ{col 25}{c |} Coefficient{col 37}  std. err.{col 49}      t{col 57}   P>|t|{col 65}     [95% con{col 78}f. interval]
{hline 24}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 13}satis_head {c |}{col 25}{res}{space 2} .0985949{col 37}{space 2} .2888707{col 48}{space 1}    0.34{col 57}{space 3}0.733{col 65}{space 4}-.4676146{col 78}{space 3} .6648045
{txt}{space 15}thirties {c |}{col 25}{res}{space 2}  .014686{col 37}{space 2}  .011628{col 48}{space 1}    1.26{col 57}{space 3}0.207{col 65}{space 4}-.0081058{col 78}{space 3} .0374779
{txt}{space 15}fourties {c |}{col 25}{res}{space 2} .0231405{col 37}{space 2} .0158396{col 48}{space 1}    1.46{col 57}{space 3}0.144{col 65}{space 4}-.0079064{col 78}{space 3} .0541875
{txt}{space 16}fifties {c |}{col 25}{res}{space 2} .0537338{col 37}{space 2} .0150404{col 48}{space 1}    3.57{col 57}{space 3}0.000{col 65}{space 4} .0242534{col 78}{space 3} .0832142
{txt}{space 16}sixties {c |}{col 25}{res}{space 2} .0751907{col 37}{space 2} .0149042{col 48}{space 1}    5.04{col 57}{space 3}0.000{col 65}{space 4} .0459772{col 78}{space 3} .1044041
{txt}{space 14}seventies {c |}{col 25}{res}{space 2} .0914929{col 37}{space 2} .0107404{col 48}{space 1}    8.52{col 57}{space 3}0.000{col 65}{space 4} .0704408{col 78}{space 3}  .112545
{txt}{space 8}income2quartile {c |}{col 25}{res}{space 2} .0277008{col 37}{space 2} .0080783{col 48}{space 1}    3.43{col 57}{space 3}0.001{col 65}{space 4} .0118668{col 78}{space 3} .0435349
{txt}{space 8}income3quartile {c |}{col 25}{res}{space 2} .0442341{col 37}{space 2} .0096813{col 48}{space 1}    4.57{col 57}{space 3}0.000{col 65}{space 4}  .025258{col 78}{space 3} .0632102
{txt}{space 8}income4quartile {c |}{col 25}{res}{space 2} .0491149{col 37}{space 2} .0143208{col 48}{space 1}    3.43{col 57}{space 3}0.001{col 65}{space 4} .0210449{col 78}{space 3} .0771848
{txt}{space 9}incomenoanswer {c |}{col 25}{res}{space 2} .0130808{col 37}{space 2} .0100828{col 48}{space 1}    1.30{col 57}{space 3}0.195{col 65}{space 4}-.0066823{col 78}{space 3} .0328439
{txt}{space 17}female {c |}{col 25}{res}{space 2}-.0120843{col 37}{space 2} .0055964{col 48}{space 1}   -2.16{col 57}{space 3}0.031{col 65}{space 4}-.0230537{col 78}{space 3} -.001115
{txt}{space 13}highschool {c |}{col 25}{res}{space 2} .0268864{col 37}{space 2} .0075016{col 48}{space 1}    3.58{col 57}{space 3}0.000{col 65}{space 4} .0121827{col 78}{space 3}   .04159
{txt}{space 16}college {c |}{col 25}{res}{space 2} .0661092{col 37}{space 2} .0069067{col 48}{space 1}    9.57{col 57}{space 3}0.000{col 65}{space 4} .0525715{col 78}{space 3} .0796468
{txt}{space 13}noreligion {c |}{col 25}{res}{space 2} .0370215{col 37}{space 2} .0122446{col 48}{space 1}    3.02{col 57}{space 3}0.003{col 65}{space 4} .0130211{col 78}{space 3} .0610218
{txt}{space 3}christiannotcatholic {c |}{col 25}{res}{space 2} .0286438{col 37}{space 2} .0205753{col 48}{space 1}    1.39{col 57}{space 3}0.164{col 65}{space 4}-.0116854{col 78}{space 3} .0689731
{txt}{space 15}catholic {c |}{col 25}{res}{space 2} .0322926{col 37}{space 2} .0125095{col 48}{space 1}    2.58{col 57}{space 3}0.010{col 65}{space 4} .0077731{col 78}{space 3} .0568122
{txt}{space 9}fulltimeworker {c |}{col 25}{res}{space 2} .0170315{col 37}{space 2} .0912916{col 48}{space 1}    0.19{col 57}{space 3}0.852{col 65}{space 4}-.1619074{col 78}{space 3} .1959704
{txt}{space 9}parttimeworker {c |}{col 25}{res}{space 2} .0433209{col 37}{space 2} .0917491{col 48}{space 1}    0.47{col 57}{space 3}0.637{col 65}{space 4}-.1365147{col 78}{space 3} .2231565
{txt}{space 13}unemployed {c |}{col 25}{res}{space 2} .0127211{col 37}{space 2} .1021051{col 48}{space 1}    0.12{col 57}{space 3}0.901{col 65}{space 4} -.187413{col 78}{space 3} .2128551
{txt}{space 11}selfemployed {c |}{col 25}{res}{space 2} .0135141{col 37}{space 2} .1113143{col 48}{space 1}    0.12{col 57}{space 3}0.903{col 65}{space 4}-.2046707{col 78}{space 3} .2316989
{txt}{space 16}outofLF {c |}{col 25}{res}{space 2}  .030025{col 37}{space 2}  .095557{col 48}{space 1}    0.31{col 57}{space 3}0.753{col 65}{space 4}-.1572744{col 78}{space 3} .2173245
{txt}{space 13}goodhealth {c |}{col 25}{res}{space 2} .0291391{col 37}{space 2} .0150514{col 48}{space 1}    1.94{col 57}{space 3}0.053{col 65}{space 4}-.0003629{col 78}{space 3} .0586411
{txt}{space 18}white {c |}{col 25}{res}{space 2} .0492993{col 37}{space 2} .0698816{col 48}{space 1}    0.71{col 57}{space 3}0.481{col 65}{space 4}-.0876742{col 78}{space 3} .1862727
{txt}{space 18}black {c |}{col 25}{res}{space 2} .0680603{col 37}{space 2} .0642664{col 48}{space 1}    1.06{col 57}{space 3}0.290{col 65}{space 4}-.0579071{col 78}{space 3} .1940277
{txt}{space 17}latino {c |}{col 25}{res}{space 2} .0966895{col 37}{space 2} .0687413{col 48}{space 1}    1.41{col 57}{space 3}0.160{col 65}{space 4}-.0380489{col 78}{space 3} .2314278
{txt}{space 18}asian {c |}{col 25}{res}{space 2} .0819406{col 37}{space 2} .0700575{col 48}{space 1}    1.17{col 57}{space 3}0.242{col 65}{space 4}-.0553777{col 78}{space 3} .2192589
{txt}{space 12}whitecollar {c |}{col 25}{res}{space 2} -.009287{col 37}{space 2} .0114691{col 48}{space 1}   -0.81{col 57}{space 3}0.418{col 65}{space 4}-.0317673{col 78}{space 3} .0131933
{txt}{space 13}bluecollar {c |}{col 25}{res}{space 2}-.0178858{col 37}{space 2} .0198178{col 48}{space 1}   -0.90{col 57}{space 3}0.367{col 65}{space 4}-.0567303{col 78}{space 3} .0209587
{txt}{space 10}serviceworker {c |}{col 25}{res}{space 2}-.0110622{col 37}{space 2} .0110014{col 48}{space 1}   -1.01{col 57}{space 3}0.315{col 65}{space 4}-.0326258{col 78}{space 3} .0105014
{txt}{space 12}mv_thirties {c |}{col 25}{res}{space 2}        0{col 37}{txt}  (omitted)
{space 12}mv_fourties {c |}{col 25}{res}{space 2}        0{col 37}{txt}  (omitted)
{space 13}mv_fifties {c |}{col 25}{res}{space 2}        0{col 37}{txt}  (omitted)
{space 13}mv_sixties {c |}{col 25}{res}{space 2}        0{col 37}{txt}  (omitted)
{space 11}mv_seventies {c |}{col 25}{res}{space 2}        0{col 37}{txt}  (omitted)
{space 5}mv_income2quartile {c |}{col 25}{res}{space 2}        0{col 37}{txt}  (omitted)
{space 5}mv_income3quartile {c |}{col 25}{res}{space 2}        0{col 37}{txt}  (omitted)
{space 5}mv_income4quartile {c |}{col 25}{res}{space 2}        0{col 37}{txt}  (omitted)
{space 6}mv_incomenoanswer {c |}{col 25}{res}{space 2} .0012015{col 37}{space 2} .0249247{col 48}{space 1}    0.05{col 57}{space 3}0.962{col 65}{space 4}-.0476528{col 78}{space 3} .0500559
{txt}{space 14}mv_female {c |}{col 25}{res}{space 2}        0{col 37}{txt}  (omitted)
{space 10}mv_highschool {c |}{col 25}{res}{space 2} .0247202{col 37}{space 2} .0176558{col 48}{space 1}    1.40{col 57}{space 3}0.161{col 65}{space 4}-.0098865{col 78}{space 3} .0593269
{txt}{space 13}mv_college {c |}{col 25}{res}{space 2}        0{col 37}{txt}  (omitted)
{space 10}mv_noreligion {c |}{col 25}{res}{space 2} .0269128{col 37}{space 2} .0269568{col 48}{space 1}    1.00{col 57}{space 3}0.318{col 65}{space 4}-.0259246{col 78}{space 3} .0797503
{txt}mv_christiannotcatholic {c |}{col 25}{res}{space 2}        0{col 37}{txt}  (omitted)
{space 12}mv_catholic {c |}{col 25}{res}{space 2}        0{col 37}{txt}  (omitted)
{space 6}mv_fulltimeworker {c |}{col 25}{res}{space 2}        0{col 37}{txt}  (omitted)
{space 6}mv_parttimeworker {c |}{col 25}{res}{space 2} .0399262{col 37}{space 2} .0870618{col 48}{space 1}    0.46{col 57}{space 3}0.647{col 65}{space 4} -.130722{col 78}{space 3} .2105743
{txt}{space 10}mv_unemployed {c |}{col 25}{res}{space 2}        0{col 37}{txt}  (omitted)
{space 8}mv_selfemployed {c |}{col 25}{res}{space 2} -.065779{col 37}{space 2}   .08893{col 48}{space 1}   -0.74{col 57}{space 3}0.460{col 65}{space 4}-.2400888{col 78}{space 3} .1085309
{txt}{space 13}mv_outofLF {c |}{col 25}{res}{space 2}        0{col 37}{txt}  (omitted)
{space 10}mv_goodhealth {c |}{col 25}{res}{space 2}-.2211187{col 37}{space 2} .1543247{col 48}{space 1}   -1.43{col 57}{space 3}0.152{col 65}{space 4}-.5236075{col 78}{space 3} .0813702
{txt}{space 15}mv_white {c |}{col 25}{res}{space 2} .1289081{col 37}{space 2}  .124096{col 48}{space 1}    1.04{col 57}{space 3}0.299{col 65}{space 4}  -.11433{col 78}{space 3} .3721461
{txt}{space 15}mv_black {c |}{col 25}{res}{space 2}        0{col 37}{txt}  (omitted)
{space 14}mv_latino {c |}{col 25}{res}{space 2}        0{col 37}{txt}  (omitted)
{space 15}mv_asian {c |}{col 25}{res}{space 2}        0{col 37}{txt}  (omitted)
{space 9}mv_whitecollar {c |}{col 25}{res}{space 2} -.068844{col 37}{space 2} .0883654{col 48}{space 1}   -0.78{col 57}{space 3}0.436{col 65}{space 4}-.2420473{col 78}{space 3} .1043593
{txt}{space 10}mv_bluecollar {c |}{col 25}{res}{space 2}        0{col 37}{txt}  (omitted)
{space 7}mv_serviceworker {c |}{col 25}{res}{space 2}        0{col 37}{txt}  (omitted)
{space 23} {c |}
{space 16}country {c |}
{space 15}Austria  {c |}{col 25}{res}{space 2} .1210141{col 37}{space 2} .1607766{col 48}{space 1}    0.75{col 57}{space 3}0.452{col 65}{space 4}-.1941208{col 78}{space 3} .4361491
{txt}{space 16}France  {c |}{col 25}{res}{space 2}        0{col 37}{txt}  (omitted)
{space 15}Germany  {c |}{col 25}{res}{space 2} .0618205{col 37}{space 2} .1721453{col 48}{space 1}    0.36{col 57}{space 3}0.720{col 65}{space 4}-.2755981{col 78}{space 3}  .399239
{txt}{space 17}Italy  {c |}{col 25}{res}{space 2}  .107191{col 37}{space 2} .1553619{col 48}{space 1}    0.69{col 57}{space 3}0.490{col 65}{space 4}-.1973306{col 78}{space 3} .4117127
{txt}{space 11}New_Zealand  {c |}{col 25}{res}{space 2}        0{col 37}{txt}  (omitted)
{space 17}Spain  {c |}{col 25}{res}{space 2}        0{col 37}{txt}  (omitted)
{space 16}Sweden  {c |}{col 25}{res}{space 2}-.0661314{col 37}{space 2} .0262872{col 48}{space 1}   -2.52{col 57}{space 3}0.012{col 65}{space 4}-.1176565{col 78}{space 3}-.0146064
{txt}{space 20}UK  {c |}{col 25}{res}{space 2}        0{col 37}{txt}  (omitted)
{space 20}US  {c |}{col 25}{res}{space 2}        0{col 37}{txt}  (omitted)
{space 23} {c |}
{space 18}_cons {c |}{col 25}{res}{space 2} .6582482{col 37}{space 2} .0634126{col 48}{space 1}   10.38{col 57}{space 3}0.000{col 65}{space 4} .5339544{col 78}{space 3} .7825419
{txt}{hline 24}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{help ivreg2##idtest:Underidentification test} (Kleibergen-Paap rk LM statistic):{res}{col 71}  11.768
{txt}{col 52}Chi-sq({res}1{txt}) P-val =  {res}{col 73}0.0006
{txt}{hline 78}
{help ivreg2##widtest:Weak identification test} (Cragg-Donald Wald F statistic):{res}{col 71}  11.720
{txt}                         (Kleibergen-Paap rk Wald F statistic):{res}{col 71}  11.768
{txt}Stock-Yogo weak ID test critical values:{res}{txt}{col 42}10% maximal IV size{res}{col 73} 16.38
{txt}{col 42}15% maximal IV size{res}{col 73}  8.96
{txt}{col 42}20% maximal IV size{res}{col 73}  6.66
{txt}{col 42}25% maximal IV size{res}{col 73}  5.53
{txt}Source: Stock-Yogo (2005).  Reproduced by permission.
NB: Critical values are for Cragg-Donald F statistic and i.i.d. errors.
{hline 78}
{help ivreg2##overidtests:Hansen J statistic} (overidentification test of all instruments):{res}{col 71}   0.000
{txt}{col 50}(equation exactly identified)
{hline 78}
Instrumented:{col 23}satis_head
Included instruments:{col 23}thirties fourties fifties sixties seventies
{col 23}income2quartile income3quartile income4quartile
{col 23}incomenoanswer female highschool college noreligion
{col 23}christiannotcatholic catholic fulltimeworker
{col 23}parttimeworker unemployed selfemployed outofLF goodhealth
{col 23}white black latino asian whitecollar bluecollar
{col 23}serviceworker mv_incomenoanswer mv_highschool
{col 23}mv_noreligion mv_parttimeworker mv_selfemployed
{col 23}mv_goodhealth mv_white mv_whitecollar 2.country 5.country
{col 23}6.country 10.country
Excluded instruments:{col 23}treatc
Dropped collinear:{col 23}mv_thirties mv_fourties mv_fifties mv_sixties
{col 23}mv_seventies mv_income2quartile mv_income3quartile
{col 23}mv_income4quartile mv_female mv_college
{col 23}mv_christiannotcatholic mv_catholic mv_fulltimeworker
{col 23}mv_unemployed mv_outofLF mv_black mv_latino mv_asian
{col 23}mv_bluecollar mv_serviceworker 4.country 7.country
{col 23}9.country 11.country 12.country
{hline 78}
({res}est8{txt} stored)

{com}. 
. estadd scalar fstat = e(widstat)

{txt}added scalar:
              e(fstat) =  {res}11.767823
{txt}
{com}. estadd local IV "SumIV", replace

{txt}added macro:
                 e(IV) : "{res:SumIV}"

{com}. estadd local CFE "X", replace

{txt}added macro:
                e(CFE) : "{res:X}"

{com}. estadd local Controls "X", replace

{txt}added macro:
           e(Controls) : "{res:X}"

{com}. get_mean

{txt}added scalar:
                 e(av) =  {res}.907
{txt}
{com}. 
. esttab using "3_output/2_OA/Tables/TableE10.tex", depvar keep(satis_head) ///
>         label replace wrap ///
>         cells(b(star fmt(%15.3fc)) se(par fmt(%15.3fc))) interaction(" $\times$ ") ///
>         star(* 0.10 ** 0.05 *** 0.01) ///
>         stats(Controls CFE N av IV fstat , layout(@ @ @ @ @ @) fmt(%15s %15s %15.0fc %12.3f %9.3f %9.3f %9.3f ) ///
>         labels("Individual controls" "Country FE" Observations "Outcome mean" "Instruments" "F-statistic" )) ////
>         collabels(none) mlabels(none) ///
>         mgroups("Strong leader" "Experts" "Army" "Democracy" , pattern(1 0 1 0 1 0 1 0 ) ///
>         prefix(\multicolumn{c -(}@span{c )-}{c -(}c{c )-}{c -(}) suffix({c )-}) span erepeat(\cmidrule(lr){c -(}@span{c )-}))
{res}{txt}(output written to {browse  `"3_output/2_OA/Tables/TableE10.tex"'})

{com}. 
. restore
{txt}
{com}. 
. 
. **# Table E.12: Impact on satisfaction with democracy, depending on exposure to Covid-19 
. 
. ** predicted GDP loss higher than 5%
. gen econloss_country=1 if inlist(country_str, "Austria", "UK","Italy", "France", "Spain")
{txt}(9,019 missing values generated)

{com}. replace econloss_country=0 if inlist(country_str, "Australia",  "Sweden", "Germany", "Brazil",  "US",  "Poland", "New_Zealand")
{txt}(9,019 real changes made)

{com}. 
. ** deaths per capita larger than 2 per 10,000. 
. gen highmort_country=1 if inlist(country_str,  "France", "Italy", "UK", "US", "Sweden", "Brazil" ,   "Spain")
{txt}(6,011 missing values generated)

{com}. replace highmort_country=0 if inlist(country_str, "Austria", "Australia", "New_Zealand","Germany", "Poland")
{txt}(6,011 real changes made)

{com}. 
. assert econloss_country !=. & highmort_country!=.
{txt}
{com}. la var highmort_country "High mortality country"
{txt}
{com}. la var econloss_country "High economic losses country"
{txt}
{com}. 
. gen satis_head_highmort_country = satis_head*highmort_country
{txt}
{com}. la var satis_head_highmort_country "High mortality $\times$ satisfaction with the head of government"
{txt}
{com}. gen satis_head_econloss_country = satis_head*econloss_country 
{txt}
{com}. la var satis_head_econloss_country "High econ losses $\times$ satisfaction with the head of government"
{txt}
{com}. 
. 
. eststo clear
{txt}
{com}. 
. /* initial coefficient */
. eststo: ivreg2 satis_dem ///
> (satis_head = healthc econc), small robust 
{res}
{txt}IV (2SLS) estimation
{hline 20}

Estimates efficient for homoskedasticity only
Statistics robust to heteroskedasticity

{col 55}Number of obs = {res}   22541
{txt}{col 55}F(  1, 22539) = {res}    4.68
{txt}{col 55}Prob > F      = {res}  0.0305
{txt}Total (centered) SS     = {res} 1635.679721{txt}{col 55}Centered R2   = {res}  0.3943
{txt}Total (uncentered) SS   = {res} 7268.530045{txt}{col 55}Uncentered R2 = {res}  0.8637
{txt}Residual SS             = {res} 990.7231405{txt}{col 55}Root MSE      = {res}   .2097

{txt}{hline 13}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 14}{c |}{col 26}    Robust
{col 1}   satis_dem{col 14}{c |} Coefficient{col 26}  std. err.{col 38}      t{col 46}   P>|t|{col 54}     [95% con{col 67}f. interval]
{hline 13}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 2}satis_head {c |}{col 14}{res}{space 2} .4760176{col 26}{space 2} .2199803{col 37}{space 1}    2.16{col 46}{space 3}0.030{col 54}{space 4} .0448409{col 67}{space 3} .9071942
{txt}{space 7}_cons {c |}{col 14}{res}{space 2} .2817841{col 26}{space 2} .1007952{col 37}{space 1}    2.80{col 46}{space 3}0.005{col 54}{space 4} .0842185{col 67}{space 3} .4793497
{txt}{hline 13}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{help ivreg2##idtest:Underidentification test} (Kleibergen-Paap rk LM statistic):{res}{col 71}   9.136
{txt}{col 52}Chi-sq({res}2{txt}) P-val =  {res}{col 73}0.0104
{txt}{hline 78}
{help ivreg2##widtest:Weak identification test} (Cragg-Donald Wald F statistic):{res}{col 71}   4.567
{txt}                         (Kleibergen-Paap rk Wald F statistic):{res}{col 71}   4.573
{txt}Stock-Yogo weak ID test critical values:{res}{txt}{col 42}10% maximal IV size{res}{col 73} 19.93
{txt}{col 42}15% maximal IV size{res}{col 73} 11.59
{txt}{col 42}20% maximal IV size{res}{col 73}  8.75
{txt}{col 42}25% maximal IV size{res}{col 73}  7.25
{txt}Source: Stock-Yogo (2005).  Reproduced by permission.
NB: Critical values are for Cragg-Donald F statistic and i.i.d. errors.
{hline 78}
{help ivreg2##overidtests:Hansen J statistic} (overidentification test of all instruments):{res}{col 71}   0.215
{txt}{col 52}Chi-sq({res}1{txt}) P-val =  {res}{col 73}0.6429
{txt}{hline 78}
Instrumented:{col 23}satis_head
Excluded instruments:{col 23}healthc econc
{hline 78}
({res}est1{txt} stored)

{com}. estadd scalar fstat = e(widstat)

{txt}added scalar:
              e(fstat) =  {res}4.5733259
{txt}
{com}. estadd local IV "2 SumIVs"

{txt}added macro:
                 e(IV) : "{res:2 SumIVs}"

{com}. get_mean

{txt}added scalar:
                 e(av) =  {res}.5
{txt}
{com}. 
. /* Adding interaction with mortality */
. eststo: ivreg2 satis_dem ///
>         (satis_head satis_head_highmort_country  = (c.healthc c.econc)#i.highmort_country) highmort_country, small robust 
{res}
{txt}IV (2SLS) estimation
{hline 20}

Estimates efficient for homoskedasticity only
Statistics robust to heteroskedasticity

{col 55}Number of obs = {res}   22541
{txt}{col 55}F(  3, 22537) = {res}  461.70
{txt}{col 55}Prob > F      = {res}  0.0000
{txt}Total (centered) SS     = {res} 1635.679721{txt}{col 55}Centered R2   = {res}  0.4014
{txt}Total (uncentered) SS   = {res} 7268.530045{txt}{col 55}Uncentered R2 = {res}  0.8653
{txt}Residual SS             = {res} 979.1725235{txt}{col 55}Root MSE      = {res}   .2084

{txt}{hline 28}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 29}{c |}{col 41}    Robust
{col 1}                  satis_dem{col 29}{c |} Coefficient{col 41}  std. err.{col 53}      t{col 61}   P>|t|{col 69}     [95% con{col 82}f. interval]
{hline 28}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 17}satis_head {c |}{col 29}{res}{space 2} .6657722{col 41}{space 2} .3532343{col 52}{space 1}    1.88{col 61}{space 3}0.059{col 69}{space 4}-.0265914{col 82}{space 3} 1.358136
{txt}satis_head_highmort_country {c |}{col 29}{res}{space 2} -.176392{col 41}{space 2} .4329384{col 52}{space 1}   -0.41{col 61}{space 3}0.684{col 69}{space 4}-1.024981{col 82}{space 3} .6721972
{txt}{space 11}highmort_country {c |}{col 29}{res}{space 2} .0759732{col 41}{space 2} .2307166{col 52}{space 1}    0.33{col 61}{space 3}0.742{col 69}{space 4}-.3762472{col 82}{space 3} .5281937
{txt}{space 22}_cons {c |}{col 29}{res}{space 2} .1924651{col 41}{space 2} .2063332{col 52}{space 1}    0.93{col 61}{space 3}0.351{col 69}{space 4}-.2119621{col 82}{space 3} .5968924
{txt}{hline 28}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{help ivreg2##idtest:Underidentification test} (Kleibergen-Paap rk LM statistic):{res}{col 71}   8.057
{txt}{col 52}Chi-sq({res}3{txt}) P-val =  {res}{col 73}0.0448
{txt}{hline 78}
{help ivreg2##widtest:Weak identification test} (Cragg-Donald Wald F statistic):{res}{col 71}   2.734
{txt}                         (Kleibergen-Paap rk Wald F statistic):{res}{col 71}   2.016
{txt}Stock-Yogo weak ID test critical values:{res}{txt}{col 42} 5% maximal IV relative bias{res}{col 73} 11.04
{txt}{col 42}10% maximal IV relative bias{res}{col 73}  7.56
{txt}{col 42}20% maximal IV relative bias{res}{col 73}  5.57
{txt}{col 42}30% maximal IV relative bias{res}{col 73}  4.73
{txt}{col 42}10% maximal IV size{res}{col 73} 16.87
{txt}{col 42}15% maximal IV size{res}{col 73}  9.93
{txt}{col 42}20% maximal IV size{res}{col 73}  7.54
{txt}{col 42}25% maximal IV size{res}{col 73}  6.28
{txt}Source: Stock-Yogo (2005).  Reproduced by permission.
NB: Critical values are for Cragg-Donald F statistic and i.i.d. errors.
{hline 78}
{help ivreg2##overidtests:Hansen J statistic} (overidentification test of all instruments):{res}{col 71}   0.861
{txt}{col 52}Chi-sq({res}2{txt}) P-val =  {res}{col 73}0.6501
{txt}{hline 78}
Instrumented:{col 23}satis_head satis_head_highmort_country
Included instruments:{col 23}highmort_country
Excluded instruments:{col 23}0b.highmort_country#c.healthc
{col 23}1.highmort_country#c.healthc 0b.highmort_country#c.econc
{col 23}1.highmort_country#c.econc
{hline 78}
({res}est2{txt} stored)

{com}. estadd scalar fstat = e(widstat)

{txt}added scalar:
              e(fstat) =  {res}2.0161693
{txt}
{com}. estadd local IV "2 SumIVs"

{txt}added macro:
                 e(IV) : "{res:2 SumIVs}"

{com}. get_mean

{txt}added scalar:
                 e(av) =  {res}.5
{txt}
{com}. lincom satis_head + satis_head_highmort_country 

{p 0 7}{space 1}{text:( 1)}{space 1} {res}satis_head + satis_head_highmort_country = 0{p_end}

{txt}{hline 13}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 1}   satis_dem{col 14}{c |} Coefficient{col 26}  Std. err.{col 38}      t{col 46}   P>|t|{col 54}     [95% con{col 67}f. interval]
{hline 13}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 9}(1) {c |}{col 14}{res}{space 2} .4893803{col 26}{space 2} .2503221{col 37}{space 1}    1.96{col 46}{space 3}0.051{col 54}{space 4}-.0012684{col 67}{space 3} .9800289
{txt}{hline 13}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}

{com}. local ldifhealth = round(r(estimate), 0.001)
{txt}
{com}. estadd local difhealth "$`ldifhealth'^{c -(}*{c )-}$"

{txt}added macro:
          e(difhealth) : "{res:$.489^{*}$}"

{com}. estadd scalar sddifhealth = r(se) 

{txt}added scalar:
        e(sddifhealth) =  {res}.25032211
{txt}
{com}. 
. /* Adding interaction with economic losses */
. eststo: ivreg2 satis_dem ///
> (satis_head satis_head_econloss_country = (c.healthc c.econc)#i.econloss_country) econloss_country, small robust 
{res}
{txt}IV (2SLS) estimation
{hline 20}

Estimates efficient for homoskedasticity only
Statistics robust to heteroskedasticity

{col 55}Number of obs = {res}   22541
{txt}{col 55}F(  3, 22537) = {res}  222.36
{txt}{col 55}Prob > F      = {res}  0.0000
{txt}Total (centered) SS     = {res} 1635.679721{txt}{col 55}Centered R2   = {res}  0.3313
{txt}Total (uncentered) SS   = {res} 7268.530045{txt}{col 55}Uncentered R2 = {res}  0.8495
{txt}Residual SS             = {res} 1093.787784{txt}{col 55}Root MSE      = {res}   .2203

{txt}{hline 28}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 29}{c |}{col 41}    Robust
{col 1}                  satis_dem{col 29}{c |} Coefficient{col 41}  std. err.{col 53}      t{col 61}   P>|t|{col 69}     [95% con{col 82}f. interval]
{hline 28}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 17}satis_head {c |}{col 29}{res}{space 2} .8063708{col 41}{space 2} .3637692{col 52}{space 1}    2.22{col 61}{space 3}0.027{col 69}{space 4}  .093358{col 82}{space 3} 1.519383
{txt}satis_head_econloss_country {c |}{col 29}{res}{space 2}-.3795224{col 41}{space 2} .4065055{col 52}{space 1}   -0.93{col 61}{space 3}0.351{col 69}{space 4}-1.176301{col 82}{space 3} .4172565
{txt}{space 11}econloss_country {c |}{col 29}{res}{space 2} .1554643{col 41}{space 2} .2020503{col 52}{space 1}    0.77{col 61}{space 3}0.442{col 69}{space 4}-.2405684{col 82}{space 3} .5514969
{txt}{space 22}_cons {c |}{col 29}{res}{space 2}  .132956{col 41}{space 2} .1870684{col 52}{space 1}    0.71{col 61}{space 3}0.477{col 69}{space 4}-.2337111{col 82}{space 3} .4996231
{txt}{hline 28}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{help ivreg2##idtest:Underidentification test} (Kleibergen-Paap rk LM statistic):{res}{col 71}   3.879
{txt}{col 52}Chi-sq({res}3{txt}) P-val =  {res}{col 73}0.2749
{txt}{hline 78}
{help ivreg2##widtest:Weak identification test} (Cragg-Donald Wald F statistic):{res}{col 71}   2.428
{txt}                         (Kleibergen-Paap rk Wald F statistic):{res}{col 71}   0.970
{txt}Stock-Yogo weak ID test critical values:{res}{txt}{col 42} 5% maximal IV relative bias{res}{col 73} 11.04
{txt}{col 42}10% maximal IV relative bias{res}{col 73}  7.56
{txt}{col 42}20% maximal IV relative bias{res}{col 73}  5.57
{txt}{col 42}30% maximal IV relative bias{res}{col 73}  4.73
{txt}{col 42}10% maximal IV size{res}{col 73} 16.87
{txt}{col 42}15% maximal IV size{res}{col 73}  9.93
{txt}{col 42}20% maximal IV size{res}{col 73}  7.54
{txt}{col 42}25% maximal IV size{res}{col 73}  6.28
{txt}Source: Stock-Yogo (2005).  Reproduced by permission.
NB: Critical values are for Cragg-Donald F statistic and i.i.d. errors.
{hline 78}
{help ivreg2##overidtests:Hansen J statistic} (overidentification test of all instruments):{res}{col 71}   2.595
{txt}{col 52}Chi-sq({res}2{txt}) P-val =  {res}{col 73}0.2732
{txt}{hline 78}
Instrumented:{col 23}satis_head satis_head_econloss_country
Included instruments:{col 23}econloss_country
Excluded instruments:{col 23}0b.econloss_country#c.healthc
{col 23}1.econloss_country#c.healthc 0b.econloss_country#c.econc
{col 23}1.econloss_country#c.econc
{hline 78}
({res}est3{txt} stored)

{com}. estadd scalar fstat = e(widstat)

{txt}added scalar:
              e(fstat) =  {res}.97003315
{txt}
{com}. estadd local IV "2 SumIVs"

{txt}added macro:
                 e(IV) : "{res:2 SumIVs}"

{com}. get_mean

{txt}added scalar:
                 e(av) =  {res}.5
{txt}
{com}. lincom satis_head + satis_head_econloss_country 

{p 0 7}{space 1}{text:( 1)}{space 1} {res}satis_head + satis_head_econloss_country = 0{p_end}

{txt}{hline 13}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 1}   satis_dem{col 14}{c |} Coefficient{col 26}  Std. err.{col 38}      t{col 46}   P>|t|{col 54}     [95% con{col 67}f. interval]
{hline 13}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 9}(1) {c |}{col 14}{res}{space 2} .4268484{col 26}{space 2} .1814352{col 37}{space 1}    2.35{col 46}{space 3}0.019{col 54}{space 4} .0712229{col 67}{space 3} .7824738
{txt}{hline 13}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}

{com}. local ldifecon = round(r(estimate), 0.001)
{txt}
{com}. estadd local difecon "$`ldifecon'^{c -(}**{c )-}$"

{txt}added macro:
            e(difecon) : "{res:$.427^{**}$}"

{com}. estadd scalar sddifecon = r(se) 

{txt}added scalar:
          e(sddifecon) =  {res}.18143517
{txt}
{com}.         
. esttab using "3_output/2_OA/Tables/TableE12.tex", depvar nocons order(satis_head satis_head_highmort_country satis_head_econloss_country)  ///
>          label replace wrap ///
>         cells(b(star fmt(%15.3fc)) se(par fmt(%15.3fc))) interaction(" $\times$ ") ///
>         star(* 0.10 ** 0.05 *** 0.01) ///
>         stats(Controls CFE N av IV fstat nothing difhealth sddifhealth difecon sddifecon, layout(@ @ @ @ @ @ @ @ (@) @ (@)) fmt(%15s %15s %15.0fc %9.3f %9.3f %9.3f %9.3f ) ///
>         labels("Individual controls" "Country FE" Observations "Outcome mean" "Instruments" "F-statistic" "Linear combination of estimates" "Satisfaction with the head of government +" "High mortality $\times$ satisfaction with the head of government" "Satisfaction with the head of government +" "High economic losses $\times$ satisfaction with the head of government")) ///
>         collabels(none) mlabels(none) ///
>         mgroups("Satisfaction with democracy" , pattern(1 0 0 0) ///
>         prefix(\multicolumn{c -(}@span{c )-}{c -(}c{c )-}{c -(}) suffix({c )-}) span )
{res}{txt}(output written to {browse  `"3_output/2_OA/Tables/TableE12.tex"'})

{com}.                 
.                 
. **# Table E.14: Impact on satisfaction with democracy, treatments mentioning government - 2SLS.
. 
. la var democ "Democracy"
{txt}
{com}. la var serious_h_csqc "Very serious health consequences"
{txt}
{com}. la var serious_e_csqc "Very serious economic consequences"
{txt}
{com}. la var satis_head "Satisfaction with the head of government"
{txt}
{com}. 
. eststo clear
{txt}
{com}. /* E.14. Col.1. Initial coefficient: 16 instruments */
. eststo: ivreg2 satis_dem ///
>         (satis_head = TH1_TE1 TH1_TE2 TH1_TE3 TH1_TE4 TH2_TE1 TH2_TE2 TH2_TE3 TH2_TE4 TH3_TE1 TH3_TE2 TH3_TE3 TH3_TE4 TH4_TE1 TH4_TE2 TH4_TE3), small robust 
{res}
{txt}IV (2SLS) estimation
{hline 20}

Estimates efficient for homoskedasticity only
Statistics robust to heteroskedasticity

{col 55}Number of obs = {res}   22541
{txt}{col 55}F(  1, 22539) = {res}   12.20
{txt}{col 55}Prob > F      = {res}  0.0005
{txt}Total (centered) SS     = {res} 1635.679721{txt}{col 55}Centered R2   = {res}  0.3997
{txt}Total (uncentered) SS   = {res} 7268.530045{txt}{col 55}Uncentered R2 = {res}  0.8649
{txt}Residual SS             = {res} 981.8279294{txt}{col 55}Root MSE      = {res}   .2087

{txt}{hline 13}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 14}{c |}{col 26}    Robust
{col 1}   satis_dem{col 14}{c |} Coefficient{col 26}  std. err.{col 38}      t{col 46}   P>|t|{col 54}     [95% con{col 67}f. interval]
{hline 13}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 2}satis_head {c |}{col 14}{res}{space 2}  .521565{col 26}{space 2}  .149351{col 37}{space 1}    3.49{col 46}{space 3}0.000{col 54}{space 4} .2288268{col 67}{space 3} .8143032
{txt}{space 7}_cons {c |}{col 14}{res}{space 2} .2609145{col 26}{space 2}  .068446{col 37}{space 1}    3.81{col 46}{space 3}0.000{col 54}{space 4} .1267555{col 67}{space 3} .3950734
{txt}{hline 13}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{help ivreg2##idtest:Underidentification test} (Kleibergen-Paap rk LM statistic):{res}{col 71}  19.860
{txt}{col 52}Chi-sq({res}15{txt}) P-val =  {res}{col 73}0.1774
{txt}{hline 78}
{help ivreg2##widtest:Weak identification test} (Cragg-Donald Wald F statistic):{res}{col 71}   1.318
{txt}                         (Kleibergen-Paap rk Wald F statistic):{res}{col 71}   1.328
{txt}Stock-Yogo weak ID test critical values:{res}{txt}{col 42} 5% maximal IV relative bias{res}{col 73} 21.23
{txt}{col 42}10% maximal IV relative bias{res}{col 73} 11.51
{txt}{col 42}20% maximal IV relative bias{res}{col 73}  6.42
{txt}{col 42}30% maximal IV relative bias{res}{col 73}  4.63
{txt}{col 42}10% maximal IV size{res}{col 73} 50.39
{txt}{col 42}15% maximal IV size{res}{col 73} 26.80
{txt}{col 42}20% maximal IV size{res}{col 73} 18.72
{txt}{col 42}25% maximal IV size{res}{col 73} 14.60
{txt}Source: Stock-Yogo (2005).  Reproduced by permission.
NB: Critical values are for Cragg-Donald F statistic and i.i.d. errors.
{hline 78}
{help ivreg2##overidtests:Hansen J statistic} (overidentification test of all instruments):{res}{col 71}   7.648
{txt}{col 52}Chi-sq({res}14{txt}) P-val =  {res}{col 73}0.9068
{txt}{hline 78}
Instrumented:{col 23}satis_head
Excluded instruments:{col 23}TH1_TE1 TH1_TE2 TH1_TE3 TH1_TE4 TH2_TE1 TH2_TE2 TH2_TE3
{col 23}TH2_TE4 TH3_TE1 TH3_TE2 TH3_TE3 TH3_TE4 TH4_TE1 TH4_TE2
{col 23}TH4_TE3
{hline 78}
({res}est1{txt} stored)

{com}. 
. estimates store mod1
{txt}
{com}. estadd scalar fstat = e(widstat)

{txt}added scalar:
              e(fstat) =  {res}1.3283149
{txt}
{com}. estadd local IV "16 IVs"

{txt}added macro:
                 e(IV) : "{res:16 IVs}"

{com}. estadd local nothing nothing2 = " "

{txt}added macro:
            e(nothing) : "{res:nothing2 = " "}"

{com}. get_mean

{txt}added scalar:
                 e(av) =  {res}.5
{txt}
{com}. local coef1 = _b[satis_head]
{txt}
{com}. local se1 = _se[satis_head]
{txt}
{com}. 
. ** for Hausman later 
. qui ivreg2 satis_dem ///
>         (satis_head = TH1_TE1 TH1_TE2 TH1_TE3 TH1_TE4 TH2_TE1 TH2_TE2 TH2_TE3 TH2_TE4 TH3_TE1 TH3_TE2 TH3_TE3 TH3_TE4 TH4_TE1 TH4_TE2 TH4_TE3), small 
{txt}
{com}. estimates store mod1b
{txt}
{com}. 
. /* E.14. Col.2. Initial coefficient: 16 instruments + controls */
. eststo: ivreg2 satis_dem ///
>         (satis_head = TH1_TE1 TH1_TE2 TH1_TE3 TH1_TE4 TH2_TE1 TH2_TE2 TH2_TE3 TH2_TE4 TH3_TE1 TH3_TE2 TH3_TE3 TH3_TE4 TH4_TE1 TH4_TE2 TH4_TE3) $controls $mv_controls i.country, small robust
{txt}Warning - collinearities detected
Vars dropped:{col 21}mv_thirties mv_fourties mv_fifties mv_sixties mv_seventies
{col 21}mv_income2quartile mv_income3quartile mv_income4quartile
{col 21}mv_female mv_college mv_christiannotcatholic mv_catholic
{col 21}mv_fulltimeworker mv_unemployed mv_outofLF mv_black
{col 21}mv_latino mv_asian mv_bluecollar mv_serviceworker 4.country
{col 21}7.country 9.country 11.country 12.country
{res}
{txt}IV (2SLS) estimation
{hline 20}

Estimates efficient for homoskedasticity only
Statistics robust to heteroskedasticity

{col 55}Number of obs = {res}   22541
{txt}{col 55}F( 43, 22497) = {res}  114.64
{txt}{col 55}Prob > F      = {res}  0.0000
{txt}Total (centered) SS     = {res} 1635.679721{txt}{col 55}Centered R2   = {res}  0.4331
{txt}Total (uncentered) SS   = {res} 7268.530045{txt}{col 55}Uncentered R2 = {res}  0.8724
{txt}Residual SS             = {res} 927.3110743{txt}{col 55}Root MSE      = {res}    .203

{txt}{hline 24}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 25}{c |}{col 37}    Robust
{col 1}              satis_dem{col 25}{c |} Coefficient{col 37}  std. err.{col 49}      t{col 57}   P>|t|{col 65}     [95% con{col 78}f. interval]
{hline 24}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 13}satis_head {c |}{col 25}{res}{space 2} .5283618{col 37}{space 2} .1425757{col 48}{space 1}    3.71{col 57}{space 3}0.000{col 65}{space 4} .2489036{col 78}{space 3}   .80782
{txt}{space 15}thirties {c |}{col 25}{res}{space 2} -.015411{col 37}{space 2} .0050154{col 48}{space 1}   -3.07{col 57}{space 3}0.002{col 65}{space 4}-.0252415{col 78}{space 3}-.0055806
{txt}{space 15}fourties {c |}{col 25}{res}{space 2} -.011639{col 37}{space 2} .0064623{col 48}{space 1}   -1.80{col 57}{space 3}0.072{col 65}{space 4}-.0243056{col 78}{space 3} .0010276
{txt}{space 16}fifties {c |}{col 25}{res}{space 2}-.0108162{col 37}{space 2} .0068628{col 48}{space 1}   -1.58{col 57}{space 3}0.115{col 65}{space 4}-.0242678{col 78}{space 3} .0026354
{txt}{space 16}sixties {c |}{col 25}{res}{space 2} .0033724{col 37}{space 2} .0070662{col 48}{space 1}    0.48{col 57}{space 3}0.633{col 65}{space 4}-.0104778{col 78}{space 3} .0172225
{txt}{space 14}seventies {c |}{col 25}{res}{space 2} .0202027{col 37}{space 2} .0062979{col 48}{space 1}    3.21{col 57}{space 3}0.001{col 65}{space 4} .0078585{col 78}{space 3}  .032547
{txt}{space 8}income2quartile {c |}{col 25}{res}{space 2} .0192263{col 37}{space 2} .0046975{col 48}{space 1}    4.09{col 57}{space 3}0.000{col 65}{space 4} .0100188{col 78}{space 3} .0284338
{txt}{space 8}income3quartile {c |}{col 25}{res}{space 2} .0257428{col 37}{space 2} .0052717{col 48}{space 1}    4.88{col 57}{space 3}0.000{col 65}{space 4} .0154099{col 78}{space 3} .0360757
{txt}{space 8}income4quartile {c |}{col 25}{res}{space 2}  .041517{col 37}{space 2}  .007253{col 48}{space 1}    5.72{col 57}{space 3}0.000{col 65}{space 4} .0273006{col 78}{space 3} .0557333
{txt}{space 9}incomenoanswer {c |}{col 25}{res}{space 2} .0079892{col 37}{space 2} .0067101{col 48}{space 1}    1.19{col 57}{space 3}0.234{col 65}{space 4} -.005163{col 78}{space 3} .0211415
{txt}{space 17}female {c |}{col 25}{res}{space 2}-.0091862{col 37}{space 2} .0031529{col 48}{space 1}   -2.91{col 57}{space 3}0.004{col 65}{space 4}-.0153661{col 78}{space 3}-.0030063
{txt}{space 13}highschool {c |}{col 25}{res}{space 2} .0054292{col 37}{space 2} .0047662{col 48}{space 1}    1.14{col 57}{space 3}0.255{col 65}{space 4} -.003913{col 78}{space 3} .0147714
{txt}{space 16}college {c |}{col 25}{res}{space 2} .0270696{col 37}{space 2} .0042705{col 48}{space 1}    6.34{col 57}{space 3}0.000{col 65}{space 4} .0186992{col 78}{space 3} .0354401
{txt}{space 13}noreligion {c |}{col 25}{res}{space 2}-.0118266{col 37}{space 2} .0076726{col 48}{space 1}   -1.54{col 57}{space 3}0.123{col 65}{space 4}-.0268655{col 78}{space 3} .0032123
{txt}{space 3}christiannotcatholic {c |}{col 25}{res}{space 2}  .012457{col 37}{space 2} .0115508{col 48}{space 1}    1.08{col 57}{space 3}0.281{col 65}{space 4}-.0101833{col 78}{space 3} .0350973
{txt}{space 15}catholic {c |}{col 25}{res}{space 2} .0164308{col 37}{space 2} .0069826{col 48}{space 1}    2.35{col 57}{space 3}0.019{col 65}{space 4} .0027445{col 78}{space 3} .0301172
{txt}{space 9}fulltimeworker {c |}{col 25}{res}{space 2}-.0331149{col 37}{space 2} .0450563{col 48}{space 1}   -0.73{col 57}{space 3}0.462{col 65}{space 4}-.1214284{col 78}{space 3} .0551986
{txt}{space 9}parttimeworker {c |}{col 25}{res}{space 2}-.0069356{col 37}{space 2} .0470736{col 48}{space 1}   -0.15{col 57}{space 3}0.883{col 65}{space 4}-.0992032{col 78}{space 3}  .085332
{txt}{space 13}unemployed {c |}{col 25}{res}{space 2}-.0438316{col 37}{space 2} .0504433{col 48}{space 1}   -0.87{col 57}{space 3}0.385{col 65}{space 4}-.1427039{col 78}{space 3} .0550408
{txt}{space 11}selfemployed {c |}{col 25}{res}{space 2}-.0211849{col 37}{space 2} .0608035{col 48}{space 1}   -0.35{col 57}{space 3}0.728{col 65}{space 4}-.1403641{col 78}{space 3} .0979942
{txt}{space 16}outofLF {c |}{col 25}{res}{space 2}-.0326967{col 37}{space 2} .0467518{col 48}{space 1}   -0.70{col 57}{space 3}0.484{col 65}{space 4}-.1243334{col 78}{space 3}   .05894
{txt}{space 13}goodhealth {c |}{col 25}{res}{space 2} .0251379{col 37}{space 2} .0077078{col 48}{space 1}    3.26{col 57}{space 3}0.001{col 65}{space 4} .0100301{col 78}{space 3} .0402458
{txt}{space 18}white {c |}{col 25}{res}{space 2}-.0107805{col 37}{space 2} .0402719{col 48}{space 1}   -0.27{col 57}{space 3}0.789{col 65}{space 4}-.0897162{col 78}{space 3} .0681553
{txt}{space 18}black {c |}{col 25}{res}{space 2} .0669688{col 37}{space 2} .0387218{col 48}{space 1}    1.73{col 57}{space 3}0.084{col 65}{space 4}-.0089286{col 78}{space 3} .1428662
{txt}{space 17}latino {c |}{col 25}{res}{space 2} .0547505{col 37}{space 2} .0408108{col 48}{space 1}    1.34{col 57}{space 3}0.180{col 65}{space 4}-.0252414{col 78}{space 3} .1347425
{txt}{space 18}asian {c |}{col 25}{res}{space 2} .0344473{col 37}{space 2} .0414733{col 48}{space 1}    0.83{col 57}{space 3}0.406{col 65}{space 4}-.0468433{col 78}{space 3} .1157378
{txt}{space 12}whitecollar {c |}{col 25}{res}{space 2}  .001631{col 37}{space 2} .0076523{col 48}{space 1}    0.21{col 57}{space 3}0.831{col 65}{space 4}-.0133682{col 78}{space 3} .0166301
{txt}{space 13}bluecollar {c |}{col 25}{res}{space 2}-.0101123{col 37}{space 2} .0081638{col 48}{space 1}   -1.24{col 57}{space 3}0.215{col 65}{space 4} -.026114{col 78}{space 3} .0058893
{txt}{space 10}serviceworker {c |}{col 25}{res}{space 2} -.010438{col 37}{space 2} .0059797{col 48}{space 1}   -1.75{col 57}{space 3}0.081{col 65}{space 4}-.0221586{col 78}{space 3} .0012825
{txt}{space 12}mv_thirties {c |}{col 25}{res}{space 2}        0{col 37}{txt}  (omitted)
{space 12}mv_fourties {c |}{col 25}{res}{space 2}        0{col 37}{txt}  (omitted)
{space 13}mv_fifties {c |}{col 25}{res}{space 2}        0{col 37}{txt}  (omitted)
{space 13}mv_sixties {c |}{col 25}{res}{space 2}        0{col 37}{txt}  (omitted)
{space 11}mv_seventies {c |}{col 25}{res}{space 2}        0{col 37}{txt}  (omitted)
{space 5}mv_income2quartile {c |}{col 25}{res}{space 2}        0{col 37}{txt}  (omitted)
{space 5}mv_income3quartile {c |}{col 25}{res}{space 2}        0{col 37}{txt}  (omitted)
{space 5}mv_income4quartile {c |}{col 25}{res}{space 2}        0{col 37}{txt}  (omitted)
{space 6}mv_incomenoanswer {c |}{col 25}{res}{space 2} .0291219{col 37}{space 2} .0135794{col 48}{space 1}    2.14{col 57}{space 3}0.032{col 65}{space 4} .0025053{col 78}{space 3} .0557385
{txt}{space 14}mv_female {c |}{col 25}{res}{space 2}        0{col 37}{txt}  (omitted)
{space 10}mv_highschool {c |}{col 25}{res}{space 2} .0272225{col 37}{space 2} .0106467{col 48}{space 1}    2.56{col 57}{space 3}0.011{col 65}{space 4} .0063543{col 78}{space 3} .0480908
{txt}{space 13}mv_college {c |}{col 25}{res}{space 2}        0{col 37}{txt}  (omitted)
{space 10}mv_noreligion {c |}{col 25}{res}{space 2} .0005398{col 37}{space 2} .0233189{col 48}{space 1}    0.02{col 57}{space 3}0.982{col 65}{space 4}-.0451669{col 78}{space 3} .0462465
{txt}mv_christiannotcatholic {c |}{col 25}{res}{space 2}        0{col 37}{txt}  (omitted)
{space 12}mv_catholic {c |}{col 25}{res}{space 2}        0{col 37}{txt}  (omitted)
{space 6}mv_fulltimeworker {c |}{col 25}{res}{space 2}        0{col 37}{txt}  (omitted)
{space 6}mv_parttimeworker {c |}{col 25}{res}{space 2}-.0158404{col 37}{space 2} .0436233{col 48}{space 1}   -0.36{col 57}{space 3}0.717{col 65}{space 4}-.1013451{col 78}{space 3} .0696644
{txt}{space 10}mv_unemployed {c |}{col 25}{res}{space 2}        0{col 37}{txt}  (omitted)
{space 8}mv_selfemployed {c |}{col 25}{res}{space 2} .0308075{col 37}{space 2} .0450466{col 48}{space 1}    0.68{col 57}{space 3}0.494{col 65}{space 4}-.0574871{col 78}{space 3}  .119102
{txt}{space 13}mv_outofLF {c |}{col 25}{res}{space 2}        0{col 37}{txt}  (omitted)
{space 10}mv_goodhealth {c |}{col 25}{res}{space 2} .1184423{col 37}{space 2} .0760623{col 48}{space 1}    1.56{col 57}{space 3}0.119{col 65}{space 4}-.0306451{col 78}{space 3} .2675297
{txt}{space 15}mv_white {c |}{col 25}{res}{space 2} .0304671{col 37}{space 2} .0648308{col 48}{space 1}    0.47{col 57}{space 3}0.638{col 65}{space 4}-.0966058{col 78}{space 3} .1575399
{txt}{space 15}mv_black {c |}{col 25}{res}{space 2}        0{col 37}{txt}  (omitted)
{space 14}mv_latino {c |}{col 25}{res}{space 2}        0{col 37}{txt}  (omitted)
{space 15}mv_asian {c |}{col 25}{res}{space 2}        0{col 37}{txt}  (omitted)
{space 9}mv_whitecollar {c |}{col 25}{res}{space 2}-.0238005{col 37}{space 2} .0455043{col 48}{space 1}   -0.52{col 57}{space 3}0.601{col 65}{space 4} -.112992{col 78}{space 3}  .065391
{txt}{space 10}mv_bluecollar {c |}{col 25}{res}{space 2}        0{col 37}{txt}  (omitted)
{space 7}mv_serviceworker {c |}{col 25}{res}{space 2}        0{col 37}{txt}  (omitted)
{space 23} {c |}
{space 16}country {c |}
{space 15}Austria  {c |}{col 25}{res}{space 2} .0413313{col 37}{space 2} .0828504{col 48}{space 1}    0.50{col 57}{space 3}0.618{col 65}{space 4}-.1210612{col 78}{space 3} .2037239
{txt}{space 16}Brazil  {c |}{col 25}{res}{space 2}-.0732144{col 37}{space 2} .0149202{col 48}{space 1}   -4.91{col 57}{space 3}0.000{col 65}{space 4} -.102459{col 78}{space 3}-.0439698
{txt}{space 16}France  {c |}{col 25}{res}{space 2}        0{col 37}{txt}  (omitted)
{space 15}Germany  {c |}{col 25}{res}{space 2} .0201346{col 37}{space 2} .0877115{col 48}{space 1}    0.23{col 57}{space 3}0.818{col 65}{space 4} -.151786{col 78}{space 3} .1920553
{txt}{space 17}Italy  {c |}{col 25}{res}{space 2}-.0710875{col 37}{space 2} .0827082{col 48}{space 1}   -0.86{col 57}{space 3}0.390{col 65}{space 4}-.2332012{col 78}{space 3} .0910263
{txt}{space 11}New_Zealand  {c |}{col 25}{res}{space 2}        0{col 37}{txt}  (omitted)
{space 16}Poland  {c |}{col 25}{res}{space 2}-.0948499{col 37}{space 2} .0145928{col 48}{space 1}   -6.50{col 57}{space 3}0.000{col 65}{space 4}-.1234528{col 78}{space 3} -.066247
{txt}{space 17}Spain  {c |}{col 25}{res}{space 2}        0{col 37}{txt}  (omitted)
{space 16}Sweden  {c |}{col 25}{res}{space 2} .0625265{col 37}{space 2} .0161997{col 48}{space 1}    3.86{col 57}{space 3}0.000{col 65}{space 4}  .030774{col 78}{space 3} .0942791
{txt}{space 20}UK  {c |}{col 25}{res}{space 2}        0{col 37}{txt}  (omitted)
{space 20}US  {c |}{col 25}{res}{space 2}        0{col 37}{txt}  (omitted)
{space 23} {c |}
{space 18}_cons {c |}{col 25}{res}{space 2} .2057792{col 37}{space 2} .0387972{col 48}{space 1}    5.30{col 57}{space 3}0.000{col 65}{space 4} .1297341{col 78}{space 3} .2818243
{txt}{hline 24}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{help ivreg2##idtest:Underidentification test} (Kleibergen-Paap rk LM statistic):{res}{col 71}  23.634
{txt}{col 52}Chi-sq({res}15{txt}) P-val =  {res}{col 73}0.0716
{txt}{hline 78}
{help ivreg2##widtest:Weak identification test} (Cragg-Donald Wald F statistic):{res}{col 71}   1.548
{txt}                         (Kleibergen-Paap rk Wald F statistic):{res}{col 71}   1.578
{txt}Stock-Yogo weak ID test critical values:{res}{txt}{col 42} 5% maximal IV relative bias{res}{col 73} 21.23
{txt}{col 42}10% maximal IV relative bias{res}{col 73} 11.51
{txt}{col 42}20% maximal IV relative bias{res}{col 73}  6.42
{txt}{col 42}30% maximal IV relative bias{res}{col 73}  4.63
{txt}{col 42}10% maximal IV size{res}{col 73} 50.39
{txt}{col 42}15% maximal IV size{res}{col 73} 26.80
{txt}{col 42}20% maximal IV size{res}{col 73} 18.72
{txt}{col 42}25% maximal IV size{res}{col 73} 14.60
{txt}Source: Stock-Yogo (2005).  Reproduced by permission.
NB: Critical values are for Cragg-Donald F statistic and i.i.d. errors.
{hline 78}
{help ivreg2##overidtests:Hansen J statistic} (overidentification test of all instruments):{res}{col 71}   7.601
{txt}{col 52}Chi-sq({res}14{txt}) P-val =  {res}{col 73}0.9091
{txt}{hline 78}
Instrumented:{col 23}satis_head
Included instruments:{col 23}thirties fourties fifties sixties seventies
{col 23}income2quartile income3quartile income4quartile
{col 23}incomenoanswer female highschool college noreligion
{col 23}christiannotcatholic catholic fulltimeworker
{col 23}parttimeworker unemployed selfemployed outofLF goodhealth
{col 23}white black latino asian whitecollar bluecollar
{col 23}serviceworker mv_incomenoanswer mv_highschool
{col 23}mv_noreligion mv_parttimeworker mv_selfemployed
{col 23}mv_goodhealth mv_white mv_whitecollar 2.country 3.country
{col 23}5.country 6.country 8.country 10.country
Excluded instruments:{col 23}TH1_TE1 TH1_TE2 TH1_TE3 TH1_TE4 TH2_TE1 TH2_TE2 TH2_TE3
{col 23}TH2_TE4 TH3_TE1 TH3_TE2 TH3_TE3 TH3_TE4 TH4_TE1 TH4_TE2
{col 23}TH4_TE3
Dropped collinear:{col 23}mv_thirties mv_fourties mv_fifties mv_sixties
{col 23}mv_seventies mv_income2quartile mv_income3quartile
{col 23}mv_income4quartile mv_female mv_college
{col 23}mv_christiannotcatholic mv_catholic mv_fulltimeworker
{col 23}mv_unemployed mv_outofLF mv_black mv_latino mv_asian
{col 23}mv_bluecollar mv_serviceworker 4.country 7.country
{col 23}9.country 11.country 12.country
{hline 78}
({res}est2{txt} stored)

{com}. 
. estimates store mod2
{txt}
{com}. estadd scalar fstat = e(widstat)

{txt}added scalar:
              e(fstat) =  {res}1.5781581
{txt}
{com}. estadd local CFE "X", replace

{txt}added macro:
                e(CFE) : "{res:X}"

{com}. estadd local Controls "X", replace

{txt}added macro:
           e(Controls) : "{res:X}"

{com}. estadd local IV "16 IVs"

{txt}added macro:
                 e(IV) : "{res:16 IVs}"

{com}. estadd local nothing nothing2 = " "

{txt}added macro:
            e(nothing) : "{res:nothing2 = " "}"

{com}. local coef2 = _b[satis_head]
{txt}
{com}. local se2 = _se[satis_head]
{txt}
{com}. get_mean

{txt}added scalar:
                 e(av) =  {res}.5
{txt}
{com}. 
. * for Hausman later                                                                                                                     
. eststo: ivreg2 satis_dem ///
>         (satis_head = TH1_TE1 TH1_TE2 TH1_TE3 TH1_TE4 TH2_TE1 TH2_TE2 TH2_TE3 TH2_TE4 TH3_TE1 TH3_TE2 TH3_TE3 TH3_TE4 TH4_TE1 TH4_TE2 TH4_TE3) $controls $mv_controls i.country, small 
{txt}Warning - collinearities detected
Vars dropped:{col 21}mv_thirties mv_fourties mv_fifties mv_sixties mv_seventies
{col 21}mv_income2quartile mv_income3quartile mv_income4quartile
{col 21}mv_female mv_college mv_christiannotcatholic mv_catholic
{col 21}mv_fulltimeworker mv_unemployed mv_outofLF mv_black
{col 21}mv_latino mv_asian mv_bluecollar mv_serviceworker 4.country
{col 21}7.country 9.country 11.country 12.country
{res}
{txt}IV (2SLS) estimation
{hline 20}

Estimates efficient for homoskedasticity only
Statistics consistent for homoskedasticity only

{col 55}Number of obs = {res}   22541
{txt}{col 55}F( 43, 22497) = {res}  101.32
{txt}{col 55}Prob > F      = {res}  0.0000
{txt}Total (centered) SS     = {res} 1635.679721{txt}{col 55}Centered R2   = {res}  0.4331
{txt}Total (uncentered) SS   = {res} 7268.530045{txt}{col 55}Uncentered R2 = {res}  0.8724
{txt}Residual SS             = {res} 927.3110743{txt}{col 55}Root MSE      = {res}    .203

{txt}{hline 24}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 1}              satis_dem{col 25}{c |} Coefficient{col 37}  Std. err.{col 49}      t{col 57}   P>|t|{col 65}     [95% con{col 78}f. interval]
{hline 24}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 13}satis_head {c |}{col 25}{res}{space 2} .5283618{col 37}{space 2} .1428353{col 48}{space 1}    3.70{col 57}{space 3}0.000{col 65}{space 4} .2483946{col 78}{space 3}  .808329
{txt}{space 15}thirties {c |}{col 25}{res}{space 2} -.015411{col 37}{space 2} .0052254{col 48}{space 1}   -2.95{col 57}{space 3}0.003{col 65}{space 4}-.0256531{col 78}{space 3}-.0051689
{txt}{space 15}fourties {c |}{col 25}{res}{space 2} -.011639{col 37}{space 2} .0065916{col 48}{space 1}   -1.77{col 57}{space 3}0.077{col 65}{space 4} -.024559{col 78}{space 3} .0012811
{txt}{space 16}fifties {c |}{col 25}{res}{space 2}-.0108162{col 37}{space 2} .0069311{col 48}{space 1}   -1.56{col 57}{space 3}0.119{col 65}{space 4}-.0244016{col 78}{space 3} .0027692
{txt}{space 16}sixties {c |}{col 25}{res}{space 2} .0033724{col 37}{space 2}  .007127{col 48}{space 1}    0.47{col 57}{space 3}0.636{col 65}{space 4}-.0105971{col 78}{space 3} .0173419
{txt}{space 14}seventies {c |}{col 25}{res}{space 2} .0202027{col 37}{space 2} .0062472{col 48}{space 1}    3.23{col 57}{space 3}0.001{col 65}{space 4} .0079578{col 78}{space 3} .0324477
{txt}{space 8}income2quartile {c |}{col 25}{res}{space 2} .0192263{col 37}{space 2} .0046942{col 48}{space 1}    4.10{col 57}{space 3}0.000{col 65}{space 4} .0100254{col 78}{space 3} .0284273
{txt}{space 8}income3quartile {c |}{col 25}{res}{space 2} .0257428{col 37}{space 2} .0052582{col 48}{space 1}    4.90{col 57}{space 3}0.000{col 65}{space 4} .0154363{col 78}{space 3} .0360492
{txt}{space 8}income4quartile {c |}{col 25}{res}{space 2}  .041517{col 37}{space 2} .0072585{col 48}{space 1}    5.72{col 57}{space 3}0.000{col 65}{space 4} .0272898{col 78}{space 3} .0557441
{txt}{space 9}incomenoanswer {c |}{col 25}{res}{space 2} .0079892{col 37}{space 2} .0065878{col 48}{space 1}    1.21{col 57}{space 3}0.225{col 65}{space 4}-.0049234{col 78}{space 3} .0209019
{txt}{space 17}female {c |}{col 25}{res}{space 2}-.0091862{col 37}{space 2} .0031569{col 48}{space 1}   -2.91{col 57}{space 3}0.004{col 65}{space 4} -.015374{col 78}{space 3}-.0029985
{txt}{space 13}highschool {c |}{col 25}{res}{space 2} .0054292{col 37}{space 2} .0048207{col 48}{space 1}    1.13{col 57}{space 3}0.260{col 65}{space 4}-.0040196{col 78}{space 3}  .014878
{txt}{space 16}college {c |}{col 25}{res}{space 2} .0270696{col 37}{space 2} .0043815{col 48}{space 1}    6.18{col 57}{space 3}0.000{col 65}{space 4} .0184816{col 78}{space 3} .0356576
{txt}{space 13}noreligion {c |}{col 25}{res}{space 2}-.0118266{col 37}{space 2} .0072972{col 48}{space 1}   -1.62{col 57}{space 3}0.105{col 65}{space 4}-.0261297{col 78}{space 3} .0024765
{txt}{space 3}christiannotcatholic {c |}{col 25}{res}{space 2}  .012457{col 37}{space 2} .0112196{col 48}{space 1}    1.11{col 57}{space 3}0.267{col 65}{space 4}-.0095341{col 78}{space 3} .0344481
{txt}{space 15}catholic {c |}{col 25}{res}{space 2} .0164308{col 37}{space 2} .0066495{col 48}{space 1}    2.47{col 57}{space 3}0.013{col 65}{space 4} .0033973{col 78}{space 3} .0294644
{txt}{space 9}fulltimeworker {c |}{col 25}{res}{space 2}-.0331149{col 37}{space 2} .0452237{col 48}{space 1}   -0.73{col 57}{space 3}0.464{col 65}{space 4}-.1217566{col 78}{space 3} .0555267
{txt}{space 9}parttimeworker {c |}{col 25}{res}{space 2}-.0069356{col 37}{space 2} .0468142{col 48}{space 1}   -0.15{col 57}{space 3}0.882{col 65}{space 4}-.0986948{col 78}{space 3} .0848236
{txt}{space 13}unemployed {c |}{col 25}{res}{space 2}-.0438316{col 37}{space 2} .0506391{col 48}{space 1}   -0.87{col 57}{space 3}0.387{col 65}{space 4}-.1430877{col 78}{space 3} .0554245
{txt}{space 11}selfemployed {c |}{col 25}{res}{space 2}-.0211849{col 37}{space 2} .0582833{col 48}{space 1}   -0.36{col 57}{space 3}0.716{col 65}{space 4}-.1354243{col 78}{space 3} .0930544
{txt}{space 16}outofLF {c |}{col 25}{res}{space 2}-.0326967{col 37}{space 2} .0469876{col 48}{space 1}   -0.70{col 57}{space 3}0.487{col 65}{space 4}-.1247957{col 78}{space 3} .0594023
{txt}{space 13}goodhealth {c |}{col 25}{res}{space 2} .0251379{col 37}{space 2} .0077309{col 48}{space 1}    3.25{col 57}{space 3}0.001{col 65}{space 4} .0099848{col 78}{space 3} .0402911
{txt}{space 18}white {c |}{col 25}{res}{space 2}-.0107805{col 37}{space 2} .0329656{col 48}{space 1}   -0.33{col 57}{space 3}0.744{col 65}{space 4}-.0753953{col 78}{space 3} .0538344
{txt}{space 18}black {c |}{col 25}{res}{space 2} .0669688{col 37}{space 2} .0299214{col 48}{space 1}    2.24{col 57}{space 3}0.025{col 65}{space 4} .0083208{col 78}{space 3} .1256168
{txt}{space 17}latino {c |}{col 25}{res}{space 2} .0547505{col 37}{space 2} .0330934{col 48}{space 1}    1.65{col 57}{space 3}0.098{col 65}{space 4}-.0101147{col 78}{space 3} .1196158
{txt}{space 18}asian {c |}{col 25}{res}{space 2} .0344473{col 37}{space 2} .0342677{col 48}{space 1}    1.01{col 57}{space 3}0.315{col 65}{space 4}-.0327199{col 78}{space 3} .1016144
{txt}{space 12}whitecollar {c |}{col 25}{res}{space 2}  .001631{col 37}{space 2} .0073886{col 48}{space 1}    0.22{col 57}{space 3}0.825{col 65}{space 4}-.0128512{col 78}{space 3} .0161131
{txt}{space 13}bluecollar {c |}{col 25}{res}{space 2}-.0101123{col 37}{space 2}  .007956{col 48}{space 1}   -1.27{col 57}{space 3}0.204{col 65}{space 4}-.0257067{col 78}{space 3}  .005482
{txt}{space 10}serviceworker {c |}{col 25}{res}{space 2} -.010438{col 37}{space 2} .0059809{col 48}{space 1}   -1.75{col 57}{space 3}0.081{col 65}{space 4}-.0221609{col 78}{space 3} .0012849
{txt}{space 12}mv_thirties {c |}{col 25}{res}{space 2}        0{col 37}{txt}  (omitted)
{space 12}mv_fourties {c |}{col 25}{res}{space 2}        0{col 37}{txt}  (omitted)
{space 13}mv_fifties {c |}{col 25}{res}{space 2}        0{col 37}{txt}  (omitted)
{space 13}mv_sixties {c |}{col 25}{res}{space 2}        0{col 37}{txt}  (omitted)
{space 11}mv_seventies {c |}{col 25}{res}{space 2}        0{col 37}{txt}  (omitted)
{space 5}mv_income2quartile {c |}{col 25}{res}{space 2}        0{col 37}{txt}  (omitted)
{space 5}mv_income3quartile {c |}{col 25}{res}{space 2}        0{col 37}{txt}  (omitted)
{space 5}mv_income4quartile {c |}{col 25}{res}{space 2}        0{col 37}{txt}  (omitted)
{space 6}mv_incomenoanswer {c |}{col 25}{res}{space 2} .0291219{col 37}{space 2} .0141174{col 48}{space 1}    2.06{col 57}{space 3}0.039{col 65}{space 4} .0014508{col 78}{space 3}  .056793
{txt}{space 14}mv_female {c |}{col 25}{res}{space 2}        0{col 37}{txt}  (omitted)
{space 10}mv_highschool {c |}{col 25}{res}{space 2} .0272225{col 37}{space 2} .0114602{col 48}{space 1}    2.38{col 57}{space 3}0.018{col 65}{space 4} .0047597{col 78}{space 3} .0496853
{txt}{space 13}mv_college {c |}{col 25}{res}{space 2}        0{col 37}{txt}  (omitted)
{space 10}mv_noreligion {c |}{col 25}{res}{space 2} .0005398{col 37}{space 2} .0200025{col 48}{space 1}    0.03{col 57}{space 3}0.978{col 65}{space 4}-.0386665{col 78}{space 3}  .039746
{txt}mv_christiannotcatholic {c |}{col 25}{res}{space 2}        0{col 37}{txt}  (omitted)
{space 12}mv_catholic {c |}{col 25}{res}{space 2}        0{col 37}{txt}  (omitted)
{space 6}mv_fulltimeworker {c |}{col 25}{res}{space 2}        0{col 37}{txt}  (omitted)
{space 6}mv_parttimeworker {c |}{col 25}{res}{space 2}-.0158404{col 37}{space 2} .0432683{col 48}{space 1}   -0.37{col 57}{space 3}0.714{col 65}{space 4}-.1006493{col 78}{space 3} .0689686
{txt}{space 10}mv_unemployed {c |}{col 25}{res}{space 2}        0{col 37}{txt}  (omitted)
{space 8}mv_selfemployed {c |}{col 25}{res}{space 2} .0308075{col 37}{space 2} .0445768{col 48}{space 1}    0.69{col 57}{space 3}0.490{col 65}{space 4}-.0565661{col 78}{space 3}  .118181
{txt}{space 13}mv_outofLF {c |}{col 25}{res}{space 2}        0{col 37}{txt}  (omitted)
{space 10}mv_goodhealth {c |}{col 25}{res}{space 2} .1184423{col 37}{space 2} .0666299{col 48}{space 1}    1.78{col 57}{space 3}0.075{col 65}{space 4} -.012157{col 78}{space 3} .2490416
{txt}{space 15}mv_white {c |}{col 25}{res}{space 2} .0304671{col 37}{space 2} .0608699{col 48}{space 1}    0.50{col 57}{space 3}0.617{col 65}{space 4}-.0888421{col 78}{space 3} .1497763
{txt}{space 15}mv_black {c |}{col 25}{res}{space 2}        0{col 37}{txt}  (omitted)
{space 14}mv_latino {c |}{col 25}{res}{space 2}        0{col 37}{txt}  (omitted)
{space 15}mv_asian {c |}{col 25}{res}{space 2}        0{col 37}{txt}  (omitted)
{space 9}mv_whitecollar {c |}{col 25}{res}{space 2}-.0238005{col 37}{space 2} .0457369{col 48}{space 1}   -0.52{col 57}{space 3}0.603{col 65}{space 4} -.113448{col 78}{space 3}  .065847
{txt}{space 10}mv_bluecollar {c |}{col 25}{res}{space 2}        0{col 37}{txt}  (omitted)
{space 7}mv_serviceworker {c |}{col 25}{res}{space 2}        0{col 37}{txt}  (omitted)
{space 23} {c |}
{space 16}country {c |}
{space 15}Austria  {c |}{col 25}{res}{space 2} .0413313{col 37}{space 2} .0798054{col 48}{space 1}    0.52{col 57}{space 3}0.605{col 65}{space 4}-.1150927{col 78}{space 3} .1977554
{txt}{space 16}Brazil  {c |}{col 25}{res}{space 2}-.0732144{col 37}{space 2} .0145045{col 48}{space 1}   -5.05{col 57}{space 3}0.000{col 65}{space 4}-.1016442{col 78}{space 3}-.0447847
{txt}{space 16}France  {c |}{col 25}{res}{space 2}        0{col 37}{txt}  (omitted)
{space 15}Germany  {c |}{col 25}{res}{space 2} .0201346{col 37}{space 2} .0848288{col 48}{space 1}    0.24{col 57}{space 3}0.812{col 65}{space 4}-.1461357{col 78}{space 3} .1864049
{txt}{space 17}Italy  {c |}{col 25}{res}{space 2}-.0710875{col 37}{space 2} .0788498{col 48}{space 1}   -0.90{col 57}{space 3}0.367{col 65}{space 4}-.2256386{col 78}{space 3} .0834636
{txt}{space 11}New_Zealand  {c |}{col 25}{res}{space 2}        0{col 37}{txt}  (omitted)
{space 16}Poland  {c |}{col 25}{res}{space 2}-.0948499{col 37}{space 2} .0153271{col 48}{space 1}   -6.19{col 57}{space 3}0.000{col 65}{space 4} -.124892{col 78}{space 3}-.0648078
{txt}{space 17}Spain  {c |}{col 25}{res}{space 2}        0{col 37}{txt}  (omitted)
{space 16}Sweden  {c |}{col 25}{res}{space 2} .0625265{col 37}{space 2}  .016561{col 48}{space 1}    3.78{col 57}{space 3}0.000{col 65}{space 4} .0300659{col 78}{space 3} .0949872
{txt}{space 20}UK  {c |}{col 25}{res}{space 2}        0{col 37}{txt}  (omitted)
{space 20}US  {c |}{col 25}{res}{space 2}        0{col 37}{txt}  (omitted)
{space 23} {c |}
{space 18}_cons {c |}{col 25}{res}{space 2} .2057792{col 37}{space 2} .0316512{col 48}{space 1}    6.50{col 57}{space 3}0.000{col 65}{space 4} .1437406{col 78}{space 3} .2678177
{txt}{hline 24}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{help ivreg2##idtest:Underidentification test} (Anderson canon. corr. LM statistic):{res}{col 71}  23.262
{txt}{col 52}Chi-sq({res}15{txt}) P-val =  {res}{col 73}0.0787
{txt}{hline 78}
{help ivreg2##widtest:Weak identification test} (Cragg-Donald Wald F statistic):{res}{col 71}   1.548
{txt}Stock-Yogo weak ID test critical values:{res}{txt}{col 42} 5% maximal IV relative bias{res}{col 73} 21.23
{txt}{col 42}10% maximal IV relative bias{res}{col 73} 11.51
{txt}{col 42}20% maximal IV relative bias{res}{col 73}  6.42
{txt}{col 42}30% maximal IV relative bias{res}{col 73}  4.63
{txt}{col 42}10% maximal IV size{res}{col 73} 50.39
{txt}{col 42}15% maximal IV size{res}{col 73} 26.80
{txt}{col 42}20% maximal IV size{res}{col 73} 18.72
{txt}{col 42}25% maximal IV size{res}{col 73} 14.60
{txt}Source: Stock-Yogo (2005).  Reproduced by permission.
{hline 78}
{help ivreg2##overidtests:Sargan statistic} (overidentification test of all instruments):{res}{col 71}   7.488
{txt}{col 52}Chi-sq({res}14{txt}) P-val =  {res}{col 73}0.9143
{txt}{hline 78}
Instrumented:{col 23}satis_head
Included instruments:{col 23}thirties fourties fifties sixties seventies
{col 23}income2quartile income3quartile income4quartile
{col 23}incomenoanswer female highschool college noreligion
{col 23}christiannotcatholic catholic fulltimeworker
{col 23}parttimeworker unemployed selfemployed outofLF goodhealth
{col 23}white black latino asian whitecollar bluecollar
{col 23}serviceworker mv_incomenoanswer mv_highschool
{col 23}mv_noreligion mv_parttimeworker mv_selfemployed
{col 23}mv_goodhealth mv_white mv_whitecollar 2.country 3.country
{col 23}5.country 6.country 8.country 10.country
Excluded instruments:{col 23}TH1_TE1 TH1_TE2 TH1_TE3 TH1_TE4 TH2_TE1 TH2_TE2 TH2_TE3
{col 23}TH2_TE4 TH3_TE1 TH3_TE2 TH3_TE3 TH3_TE4 TH4_TE1 TH4_TE2
{col 23}TH4_TE3
Dropped collinear:{col 23}mv_thirties mv_fourties mv_fifties mv_sixties
{col 23}mv_seventies mv_income2quartile mv_income3quartile
{col 23}mv_income4quartile mv_female mv_college
{col 23}mv_christiannotcatholic mv_catholic mv_fulltimeworker
{col 23}mv_unemployed mv_outofLF mv_black mv_latino mv_asian
{col 23}mv_bluecollar mv_serviceworker 4.country 7.country
{col 23}9.country 11.country 12.country
{hline 78}
({res}est3{txt} stored)

{com}. estimates store mod2b
{txt}
{com}. 
. * Hausman test for the following regression 
. qui ivreg2 satis_dem ///
>         (satis_head = TE2 TE4 TH2 TH4 TH4_TE2 TH4_TE4 TH2_TE2 TH2_TE4) bad_econ_situation bad_health_situation, small
{txt}
{com}. estimates store mod3b
{txt}
{com}. hausman mod3b mod1b

{txt}{col 18}{hline 4} Coefficients {hline 4}
{col 14}{c |}{col 21}(b){col 34}(B){col 49}(b-B){col 59}sqrt(diag(V_b-V_B))
{col 14}{c |}{col 17}   mod3b    {col 30}   mod1b    {col 46}Difference{col 63}Std. err.
{hline 13}{c +}{hline 64}
{space 2}satis_head {c |}{res}{col 18} .5201297{col 31}  .521565{col 47}-.0014353{col 63} .1188905
{txt}{hline 13}{c BT}{hline 64}
{ralign 78:b = Consistent under H0 and Ha; obtained from {bf:ivreg2}.}
{ralign 78:B = Inconsistent under Ha, efficient under H0; obtained from {bf:ivreg2}.}

Test of H0: Difference in coefficients not systematic

{ralign 11:chi2({res:1})} = (b-B)'[(V_b-V_B)^(-1)](b-B)
{ralign 11:} = {res:{ralign 6:0.00}}
{ralign 11:Prob > chi2} = {res:{ralign 6:0.9904}}

{com}. local temp_hausman = r(p)
{txt}
{com}. 
. /* E.14. Col.3. 8 instruments */
. eststo: ivreg2 satis_dem ///
>         (satis_head = TE2 TE4 TH2 TH4 TH4_TE2 TH4_TE4 TH2_TE2 TH2_TE4) bad_econ_situation bad_health_situation, small robust
{res}
{txt}IV (2SLS) estimation
{hline 20}

Estimates efficient for homoskedasticity only
Statistics robust to heteroskedasticity

{col 55}Number of obs = {res}   22541
{txt}{col 55}F(  3, 22537) = {res}    4.11
{txt}{col 55}Prob > F      = {res}  0.0064
{txt}Total (centered) SS     = {res} 1635.679721{txt}{col 55}Centered R2   = {res}  0.3997
{txt}Total (uncentered) SS   = {res} 7268.530045{txt}{col 55}Uncentered R2 = {res}  0.8649
{txt}Residual SS             = {res} 981.9643471{txt}{col 55}Root MSE      = {res}   .2087

{txt}{hline 21}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 22}{c |}{col 34}    Robust
{col 1}           satis_dem{col 22}{c |} Coefficient{col 34}  std. err.{col 46}      t{col 54}   P>|t|{col 62}     [95% con{col 75}f. interval]
{hline 21}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 10}satis_head {c |}{col 22}{res}{space 2} .5201297{col 34}{space 2} .1909916{col 45}{space 1}    2.72{col 54}{space 3}0.006{col 62}{space 4} .1457729{col 75}{space 3} .8944865
{txt}{space 2}bad_econ_situation {c |}{col 22}{res}{space 2} .0000609{col 34}{space 2}  .003039{col 45}{space 1}    0.02{col 54}{space 3}0.984{col 62}{space 4}-.0058957{col 75}{space 3} .0060175
{txt}bad_health_situation {c |}{col 22}{res}{space 2}-.0007419{col 34}{space 2} .0032244{col 45}{space 1}   -0.23{col 54}{space 3}0.818{col 62}{space 4}-.0070619{col 75}{space 3} .0055782
{txt}{space 15}_cons {c |}{col 22}{res}{space 2} .2619129{col 34}{space 2} .0889915{col 45}{space 1}    2.94{col 54}{space 3}0.003{col 62}{space 4} .0874835{col 75}{space 3} .4363424
{txt}{hline 21}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{help ivreg2##idtest:Underidentification test} (Kleibergen-Paap rk LM statistic):{res}{col 71}  12.138
{txt}{col 52}Chi-sq({res}8{txt}) P-val =  {res}{col 73}0.1451
{txt}{hline 78}
{help ivreg2##widtest:Weak identification test} (Cragg-Donald Wald F statistic):{res}{col 71}   1.515
{txt}                         (Kleibergen-Paap rk Wald F statistic):{res}{col 71}   1.519
{txt}Stock-Yogo weak ID test critical values:{res}{txt}{col 42} 5% maximal IV relative bias{res}{col 73} 20.25
{txt}{col 42}10% maximal IV relative bias{res}{col 73} 11.39
{txt}{col 42}20% maximal IV relative bias{res}{col 73}  6.69
{txt}{col 42}30% maximal IV relative bias{res}{col 73}  4.99
{txt}{col 42}10% maximal IV size{res}{col 73} 33.84
{txt}{col 42}15% maximal IV size{res}{col 73} 18.54
{txt}{col 42}20% maximal IV size{res}{col 73} 13.24
{txt}{col 42}25% maximal IV size{res}{col 73} 10.50
{txt}Source: Stock-Yogo (2005).  Reproduced by permission.
NB: Critical values are for Cragg-Donald F statistic and i.i.d. errors.
{hline 78}
{help ivreg2##overidtests:Hansen J statistic} (overidentification test of all instruments):{res}{col 71}   5.019
{txt}{col 52}Chi-sq({res}7{txt}) P-val =  {res}{col 73}0.6576
{txt}{hline 78}
Instrumented:{col 23}satis_head
Included instruments:{col 23}bad_econ_situation bad_health_situation
Excluded instruments:{col 23}TE2 TE4 TH2 TH4 TH4_TE2 TH4_TE4 TH2_TE2 TH2_TE4
{hline 78}
({res}est4{txt} stored)

{com}. 
. estimates store mod3
{txt}
{com}. estadd scalar fstat = e(widstat)

{txt}added scalar:
              e(fstat) =  {res}1.5191811
{txt}
{com}. estadd local IV "8 IVs"

{txt}added macro:
                 e(IV) : "{res:8 IVs}"

{com}. estadd local nothing nothing2 = " "

{txt}added macro:
            e(nothing) : "{res:nothing2 = " "}"

{com}. estadd scalar hausman = `temp_hausman'

{txt}added scalar:
            e(hausman) =  {res}.99036795
{txt}
{com}. local coef3 = _b[satis_head]
{txt}
{com}. local se3 = _se[satis_head]
{txt}
{com}. get_mean

{txt}added scalar:
                 e(av) =  {res}.5
{txt}
{com}. 
. * Z-test 
. local z_value = abs(`coef3'-`coef1')/sqrt(`se3'^2+`se1'^2)
{txt}
{com}. di (1-normal(`z_value'))*2
{res}.99527669
{txt}
{com}. estadd scalar wald = (1-normal(`z_value'))*2

{txt}added scalar:
               e(wald) =  {res}.99527669
{txt}
{com}. 
. * Hausman test for following regression
. qui ivreg2 satis_dem ///
>         (satis_head = TE2 TE4 TH2 TH4 TH4_TE2 TH4_TE4 TH2_TE2 TH2_TE4) bad_econ_situation bad_health_situation $controls $mv_controls i.country, small 
{txt}
{com}. estimates store mod4b
{txt}
{com}. hausman mod4b mod2b

{txt}{col 18}{hline 4} Coefficients {hline 4}
{col 14}{c |}{col 21}(b){col 34}(B){col 49}(b-B){col 59}sqrt(diag(V_b-V_B))
{col 14}{c |}{col 17}   mod4b    {col 30}   mod2b    {col 46}Difference{col 63}Std. err.
{hline 13}{c +}{hline 64}
{space 2}satis_head {c |}{res}{col 18} .5359562{col 31} .5283618{col 47} .0075944{col 63} .1090789
{txt}{space 4}thirties {c |}{res}{col 18}-.0152828{col 31} -.015411{col 47} .0001282{col 63} .0014937
{txt}{space 4}fourties {c |}{res}{col 18}-.0113609{col 31} -.011639{col 47}  .000278{col 63} .0034601
{txt}{space 5}fifties {c |}{res}{col 18}-.0105287{col 31}-.0108162{col 47} .0002875{col 63} .0036837
{txt}{space 5}sixties {c |}{res}{col 18} .0036615{col 31} .0033724{col 47} .0002891{col 63} .0038877
{txt}{space 3}seventies {c |}{res}{col 18} .0203352{col 31} .0202027{col 47} .0001324{col 63} .0015177
{txt}income2qua~e {c |}{res}{col 18} .0191162{col 31} .0192263{col 47}-.0001101{col 63} .0018858
{txt}income3qua~e {c |}{res}{col 18} .0255782{col 31} .0257428{col 47}-.0001646{col 63}   .00251
{txt}income4qua~e {c |}{res}{col 18} .0412011{col 31}  .041517{col 47}-.0003159{col 63} .0045075
{txt}incomenoan~r {c |}{res}{col 18} .0080801{col 31} .0079892{col 47} .0000908{col 63} .0010469
{txt}{space 6}female {c |}{res}{col 18} -.009274{col 31}-.0091862{col 47}-.0000877{col 63} .0012167
{txt}{space 2}highschool {c |}{res}{col 18} .0053675{col 31} .0054292{col 47}-.0000617{col 63} .0009976
{txt}{space 5}college {c |}{res}{col 18}  .027062{col 31} .0270696{col 47}-7.65e-06{col 63} .0002612
{txt}{space 2}noreligion {c |}{res}{col 18}-.0115897{col 31}-.0118266{col 47} .0002368{col 63} .0034244
{txt}christiann~c {c |}{res}{col 18} .0119916{col 31}  .012457{col 47}-.0004654{col 63} .0069697
{txt}{space 4}catholic {c |}{res}{col 18} .0162852{col 31} .0164308{col 47}-.0001456{col 63}  .002334
{txt}fulltimewo~r {c |}{res}{col 18}-.0307476{col 31}-.0331149{col 47} .0023673{col 63} .0340794
{txt}parttimewo~r {c |}{res}{col 18}-.0045541{col 31}-.0069356{col 47} .0023815{col 63}  .033356
{txt}{space 2}unemployed {c |}{res}{col 18}-.0411915{col 31}-.0438316{col 47}   .00264{col 63} .0376908
{txt}selfemployed {c |}{res}{col 18}-.0183771{col 31}-.0211849{col 47} .0028078{col 63} .0404028
{txt}{space 5}outofLF {c |}{res}{col 18}-.0302239{col 31}-.0326967{col 47} .0024728{col 63} .0353708
{txt}{space 2}goodhealth {c |}{res}{col 18} .0247514{col 31} .0251379{col 47}-.0003866{col 63} .0054773
{txt}{space 7}white {c |}{res}{col 18}-.0117695{col 31}-.0107805{col 47} -.000989{col 63} .0143917
{txt}{space 7}black {c |}{res}{col 18} .0669907{col 31} .0669688{col 47} .0000219{col 63} .0014298
{txt}{space 6}latino {c |}{res}{col 18} .0545403{col 31} .0547505{col 47}-.0002102{col 63} .0040906
{txt}{space 7}asian {c |}{res}{col 18} .0338762{col 31} .0344473{col 47}-.0005711{col 63} .0081419
{txt}{space 1}whitecollar {c |}{res}{col 18}  .001764{col 31}  .001631{col 47}  .000133{col 63} .0018408
{txt}{space 2}bluecollar {c |}{res}{col 18}-.0099102{col 31}-.0101123{col 47} .0002022{col 63} .0026113
{txt}servicewor~r {c |}{res}{col 18}-.0103637{col 31} -.010438{col 47} .0000743{col 63} .0010765
{txt}mv_incomen~r {c |}{res}{col 18} .0296854{col 31} .0291219{col 47} .0005635{col 63} .0080761
{txt}mv_highsch~l {c |}{res}{col 18} .0273215{col 31} .0272225{col 47} .0000989{col 63} .0012152
{txt}mv_norelig~n {c |}{res}{col 18} .0003388{col 31} .0005398{col 47} -.000201{col 63} .0007309
{txt}mv_parttim~r {c |}{res}{col 18}-.0136055{col 31}-.0158404{col 47} .0022348{col 63} .0314695
{txt}mv_selfemp~d {c |}{res}{col 18}  .028521{col 31} .0308075{col 47}-.0022864{col 63} .0323329
{txt}mv_goodhea~h {c |}{res}{col 18}  .117283{col 31} .1184423{col 47}-.0011593{col 63} .0190951
{txt}{space 4}mv_white {c |}{res}{col 18}  .027605{col 31} .0304671{col 47}-.0028621{col 63} .0415923
{txt}mv_whiteco~r {c |}{res}{col 18}-.0260939{col 31}-.0238005{col 47}-.0022934{col 63} .0331385
{txt}{space 5}country {c |}
{space 10}2  {c |}{res}{col 18} .0374511{col 31} .0413313{col 47}-.0038802{col 63} .0562856
{txt}{space 10}3  {c |}{res}{col 18}-.0730871{col 31}-.0732144{col 47} .0001273{col 63} .0017569
{txt}{space 10}5  {c |}{res}{col 18} .0159585{col 31} .0201346{col 47}-.0041761{col 63} .0605271
{txt}{space 10}6  {c |}{res}{col 18} -.074597{col 31}-.0710875{col 47}-.0035095{col 63} .0535869
{txt}{space 10}8  {c |}{res}{col 18}-.0945902{col 31}-.0948499{col 47} .0002598{col 63} .0037365
{txt}{space 9}10  {c |}{res}{col 18}   .06209{col 31} .0625265{col 47}-.0004365{col 63} .0064382
{txt}{hline 13}{c BT}{hline 64}
{ralign 78:b = Consistent under H0 and Ha; obtained from {bf:ivreg2}.}
{ralign 78:B = Inconsistent under Ha, efficient under H0; obtained from {bf:ivreg2}.}

Test of H0: Difference in coefficients not systematic

{ralign 11:chi2({res:43})} = (b-B)'[(V_b-V_B)^(-1)](b-B)
{ralign 11:} = {res:{ralign 6:0.19}}
{ralign 11:Prob > chi2} = {res:{ralign 6:1.0000}}

{com}. local temp_hausman = r(p)
{txt}
{com}. 
. /* E.14. Col.4. 8 instruments + controls */
. eststo: ivreg2 satis_dem ///
>         (satis_head = TE2 TE4 TH2 TH4 TH4_TE2 TH4_TE4 TH2_TE2 TH2_TE4 ) bad_econ_situation bad_health_situation $controls $mv_controls i.country, small robust          
{txt}Warning - collinearities detected
Vars dropped:{col 21}mv_thirties mv_fourties mv_fifties mv_sixties mv_seventies
{col 21}mv_income2quartile mv_income3quartile mv_income4quartile
{col 21}mv_female mv_college mv_christiannotcatholic mv_catholic
{col 21}mv_fulltimeworker mv_unemployed mv_outofLF mv_black
{col 21}mv_latino mv_asian mv_bluecollar mv_serviceworker 4.country
{col 21}7.country 9.country 11.country 12.country
{res}
{txt}IV (2SLS) estimation
{hline 20}

Estimates efficient for homoskedasticity only
Statistics robust to heteroskedasticity

{col 55}Number of obs = {res}   22541
{txt}{col 55}F( 45, 22495) = {res}  109.58
{txt}{col 55}Prob > F      = {res}  0.0000
{txt}Total (centered) SS     = {res} 1635.679721{txt}{col 55}Centered R2   = {res}  0.4329
{txt}Total (uncentered) SS   = {res} 7268.530045{txt}{col 55}Uncentered R2 = {res}  0.8724
{txt}Residual SS             = {res} 927.6640345{txt}{col 55}Root MSE      = {res}   .2031

{txt}{hline 24}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 25}{c |}{col 37}    Robust
{col 1}              satis_dem{col 25}{c |} Coefficient{col 37}  std. err.{col 49}      t{col 57}   P>|t|{col 65}     [95% con{col 78}f. interval]
{hline 24}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 13}satis_head {c |}{col 25}{res}{space 2} .5359562{col 37}{space 2} .1795861{col 48}{space 1}    2.98{col 57}{space 3}0.003{col 65}{space 4}  .183955{col 78}{space 3} .8879575
{txt}{space 5}bad_econ_situation {c |}{col 25}{res}{space 2} .0005871{col 37}{space 2} .0029453{col 48}{space 1}    0.20{col 57}{space 3}0.842{col 65}{space 4} -.005186{col 78}{space 3} .0063601
{txt}{space 3}bad_health_situation {c |}{col 25}{res}{space 2} -.000939{col 37}{space 2}  .003105{col 48}{space 1}   -0.30{col 57}{space 3}0.762{col 65}{space 4} -.007025{col 78}{space 3}  .005147
{txt}{space 15}thirties {c |}{col 25}{res}{space 2}-.0152828{col 37}{space 2} .0052119{col 48}{space 1}   -2.93{col 57}{space 3}0.003{col 65}{space 4}-.0254986{col 78}{space 3} -.005067
{txt}{space 15}fourties {c |}{col 25}{res}{space 2}-.0113609{col 37}{space 2} .0073315{col 48}{space 1}   -1.55{col 57}{space 3}0.121{col 65}{space 4}-.0257312{col 78}{space 3} .0030093
{txt}{space 16}fifties {c |}{col 25}{res}{space 2}-.0105287{col 37}{space 2} .0077634{col 48}{space 1}   -1.36{col 57}{space 3}0.175{col 65}{space 4}-.0257455{col 78}{space 3} .0046881
{txt}{space 16}sixties {c |}{col 25}{res}{space 2} .0036615{col 37}{space 2} .0080189{col 48}{space 1}    0.46{col 57}{space 3}0.648{col 65}{space 4}-.0120561{col 78}{space 3} .0193792
{txt}{space 14}seventies {c |}{col 25}{res}{space 2} .0203352{col 37}{space 2}  .006476{col 48}{space 1}    3.14{col 57}{space 3}0.002{col 65}{space 4} .0076417{col 78}{space 3} .0330287
{txt}{space 8}income2quartile {c |}{col 25}{res}{space 2} .0191162{col 37}{space 2} .0050547{col 48}{space 1}    3.78{col 57}{space 3}0.000{col 65}{space 4} .0092086{col 78}{space 3} .0290237
{txt}{space 8}income3quartile {c |}{col 25}{res}{space 2} .0255782{col 37}{space 2} .0058393{col 48}{space 1}    4.38{col 57}{space 3}0.000{col 65}{space 4} .0141329{col 78}{space 3} .0370236
{txt}{space 8}income4quartile {c |}{col 25}{res}{space 2} .0412011{col 37}{space 2} .0085511{col 48}{space 1}    4.82{col 57}{space 3}0.000{col 65}{space 4} .0244403{col 78}{space 3} .0579619
{txt}{space 9}incomenoanswer {c |}{col 25}{res}{space 2} .0080801{col 37}{space 2} .0067851{col 48}{space 1}    1.19{col 57}{space 3}0.234{col 65}{space 4}-.0052193{col 78}{space 3} .0213794
{txt}{space 17}female {c |}{col 25}{res}{space 2} -.009274{col 37}{space 2} .0033727{col 48}{space 1}   -2.75{col 57}{space 3}0.006{col 65}{space 4}-.0158848{col 78}{space 3}-.0026632
{txt}{space 13}highschool {c |}{col 25}{res}{space 2} .0053675{col 37}{space 2} .0048615{col 48}{space 1}    1.10{col 57}{space 3}0.270{col 65}{space 4}-.0041613{col 78}{space 3} .0148963
{txt}{space 16}college {c |}{col 25}{res}{space 2}  .027062{col 37}{space 2} .0042738{col 48}{space 1}    6.33{col 57}{space 3}0.000{col 65}{space 4}  .018685{col 78}{space 3} .0354389
{txt}{space 13}noreligion {c |}{col 25}{res}{space 2}-.0115897{col 37}{space 2} .0083734{col 48}{space 1}   -1.38{col 57}{space 3}0.166{col 65}{space 4}-.0280021{col 78}{space 3} .0048226
{txt}{space 3}christiannotcatholic {c |}{col 25}{res}{space 2} .0119916{col 37}{space 2} .0135377{col 48}{space 1}    0.89{col 57}{space 3}0.376{col 65}{space 4}-.0145432{col 78}{space 3} .0385263
{txt}{space 15}catholic {c |}{col 25}{res}{space 2} .0162852{col 37}{space 2} .0074034{col 48}{space 1}    2.20{col 57}{space 3}0.028{col 65}{space 4}  .001774{col 78}{space 3} .0307964
{txt}{space 9}fulltimeworker {c |}{col 25}{res}{space 2}-.0307476{col 37}{space 2} .0565305{col 48}{space 1}   -0.54{col 57}{space 3}0.587{col 65}{space 4}-.1415512{col 78}{space 3}  .080056
{txt}{space 9}parttimeworker {c |}{col 25}{res}{space 2}-.0045541{col 37}{space 2} .0577893{col 48}{space 1}   -0.08{col 57}{space 3}0.937{col 65}{space 4}-.1178251{col 78}{space 3} .1087169
{txt}{space 13}unemployed {c |}{col 25}{res}{space 2}-.0411915{col 37}{space 2} .0630197{col 48}{space 1}   -0.65{col 57}{space 3}0.513{col 65}{space 4}-.1647146{col 78}{space 3} .0823315
{txt}{space 11}selfemployed {c |}{col 25}{res}{space 2}-.0183771{col 37}{space 2} .0730054{col 48}{space 1}   -0.25{col 57}{space 3}0.801{col 65}{space 4}-.1614727{col 78}{space 3} .1247185
{txt}{space 16}outofLF {c |}{col 25}{res}{space 2}-.0302239{col 37}{space 2} .0587017{col 48}{space 1}   -0.51{col 57}{space 3}0.607{col 65}{space 4}-.1452833{col 78}{space 3} .0848355
{txt}{space 13}goodhealth {c |}{col 25}{res}{space 2} .0247514{col 37}{space 2} .0094806{col 48}{space 1}    2.61{col 57}{space 3}0.009{col 65}{space 4} .0061687{col 78}{space 3}  .043334
{txt}{space 18}white {c |}{col 25}{res}{space 2}-.0117695{col 37}{space 2}  .042787{col 48}{space 1}   -0.28{col 57}{space 3}0.783{col 65}{space 4} -.095635{col 78}{space 3} .0720961
{txt}{space 18}black {c |}{col 25}{res}{space 2} .0669907{col 37}{space 2} .0388367{col 48}{space 1}    1.72{col 57}{space 3}0.085{col 65}{space 4}-.0091319{col 78}{space 3} .1431133
{txt}{space 17}latino {c |}{col 25}{res}{space 2} .0545403{col 37}{space 2} .0410887{col 48}{space 1}    1.33{col 57}{space 3}0.184{col 65}{space 4}-.0259964{col 78}{space 3} .1350771
{txt}{space 18}asian {c |}{col 25}{res}{space 2} .0338762{col 37}{space 2} .0423535{col 48}{space 1}    0.80{col 57}{space 3}0.424{col 65}{space 4}-.0491396{col 78}{space 3}  .116892
{txt}{space 12}whitecollar {c |}{col 25}{res}{space 2}  .001764{col 37}{space 2} .0078747{col 48}{space 1}    0.22{col 57}{space 3}0.823{col 65}{space 4} -.013671{col 78}{space 3}  .017199
{txt}{space 13}bluecollar {c |}{col 25}{res}{space 2}-.0099102{col 37}{space 2} .0085874{col 48}{space 1}   -1.15{col 57}{space 3}0.248{col 65}{space 4} -.026742{col 78}{space 3} .0069217
{txt}{space 10}serviceworker {c |}{col 25}{res}{space 2}-.0103637{col 37}{space 2} .0060822{col 48}{space 1}   -1.70{col 57}{space 3}0.088{col 65}{space 4}-.0222853{col 78}{space 3} .0015578
{txt}{space 12}mv_thirties {c |}{col 25}{res}{space 2}        0{col 37}{txt}  (omitted)
{space 12}mv_fourties {c |}{col 25}{res}{space 2}        0{col 37}{txt}  (omitted)
{space 13}mv_fifties {c |}{col 25}{res}{space 2}        0{col 37}{txt}  (omitted)
{space 13}mv_sixties {c |}{col 25}{res}{space 2}        0{col 37}{txt}  (omitted)
{space 11}mv_seventies {c |}{col 25}{res}{space 2}        0{col 37}{txt}  (omitted)
{space 5}mv_income2quartile {c |}{col 25}{res}{space 2}        0{col 37}{txt}  (omitted)
{space 5}mv_income3quartile {c |}{col 25}{res}{space 2}        0{col 37}{txt}  (omitted)
{space 5}mv_income4quartile {c |}{col 25}{res}{space 2}        0{col 37}{txt}  (omitted)
{space 6}mv_incomenoanswer {c |}{col 25}{res}{space 2} .0296854{col 37}{space 2} .0158468{col 48}{space 1}    1.87{col 57}{space 3}0.061{col 65}{space 4}-.0013754{col 78}{space 3} .0607462
{txt}{space 14}mv_female {c |}{col 25}{res}{space 2}        0{col 37}{txt}  (omitted)
{space 10}mv_highschool {c |}{col 25}{res}{space 2} .0273215{col 37}{space 2}  .010706{col 48}{space 1}    2.55{col 57}{space 3}0.011{col 65}{space 4} .0063369{col 78}{space 3}  .048306
{txt}{space 13}mv_college {c |}{col 25}{res}{space 2}        0{col 37}{txt}  (omitted)
{space 10}mv_noreligion {c |}{col 25}{res}{space 2} .0003388{col 37}{space 2} .0233694{col 48}{space 1}    0.01{col 57}{space 3}0.988{col 65}{space 4}-.0454669{col 78}{space 3} .0461444
{txt}mv_christiannotcatholic {c |}{col 25}{res}{space 2}        0{col 37}{txt}  (omitted)
{space 12}mv_catholic {c |}{col 25}{res}{space 2}        0{col 37}{txt}  (omitted)
{space 6}mv_fulltimeworker {c |}{col 25}{res}{space 2}        0{col 37}{txt}  (omitted)
{space 6}mv_parttimeworker {c |}{col 25}{res}{space 2}-.0136055{col 37}{space 2}  .053813{col 48}{space 1}   -0.25{col 57}{space 3}0.800{col 65}{space 4}-.1190828{col 78}{space 3} .0918717
{txt}{space 10}mv_unemployed {c |}{col 25}{res}{space 2}        0{col 37}{txt}  (omitted)
{space 8}mv_selfemployed {c |}{col 25}{res}{space 2}  .028521{col 37}{space 2} .0555268{col 48}{space 1}    0.51{col 57}{space 3}0.608{col 65}{space 4}-.0803153{col 78}{space 3} .1373574
{txt}{space 13}mv_outofLF {c |}{col 25}{res}{space 2}        0{col 37}{txt}  (omitted)
{space 10}mv_goodhealth {c |}{col 25}{res}{space 2}  .117283{col 37}{space 2} .0783013{col 48}{space 1}    1.50{col 57}{space 3}0.134{col 65}{space 4} -.036193{col 78}{space 3} .2707589
{txt}{space 15}mv_white {c |}{col 25}{res}{space 2}  .027605{col 37}{space 2} .0769882{col 48}{space 1}    0.36{col 57}{space 3}0.720{col 65}{space 4}-.1232973{col 78}{space 3} .1785073
{txt}{space 15}mv_black {c |}{col 25}{res}{space 2}        0{col 37}{txt}  (omitted)
{space 14}mv_latino {c |}{col 25}{res}{space 2}        0{col 37}{txt}  (omitted)
{space 15}mv_asian {c |}{col 25}{res}{space 2}        0{col 37}{txt}  (omitted)
{space 9}mv_whitecollar {c |}{col 25}{res}{space 2}-.0260939{col 37}{space 2} .0562957{col 48}{space 1}   -0.46{col 57}{space 3}0.643{col 65}{space 4}-.1364374{col 78}{space 3} .0842496
{txt}{space 10}mv_bluecollar {c |}{col 25}{res}{space 2}        0{col 37}{txt}  (omitted)
{space 7}mv_serviceworker {c |}{col 25}{res}{space 2}        0{col 37}{txt}  (omitted)
{space 23} {c |}
{space 16}country {c |}
{space 15}Austria  {c |}{col 25}{res}{space 2} .0374511{col 37}{space 2} .1001055{col 48}{space 1}    0.37{col 57}{space 3}0.708{col 65}{space 4}-.1587625{col 78}{space 3} .2336648
{txt}{space 16}Brazil  {c |}{col 25}{res}{space 2}-.0730871{col 37}{space 2} .0150514{col 48}{space 1}   -4.86{col 57}{space 3}0.000{col 65}{space 4} -.102589{col 78}{space 3}-.0435852
{txt}{space 16}France  {c |}{col 25}{res}{space 2}        0{col 37}{txt}  (omitted)
{space 15}Germany  {c |}{col 25}{res}{space 2} .0159585{col 37}{space 2} .1064962{col 48}{space 1}    0.15{col 57}{space 3}0.881{col 65}{space 4}-.1927814{col 78}{space 3} .2246985
{txt}{space 17}Italy  {c |}{col 25}{res}{space 2} -.074597{col 37}{space 2} .0983443{col 48}{space 1}   -0.76{col 57}{space 3}0.448{col 65}{space 4}-.2673586{col 78}{space 3} .1181646
{txt}{space 11}New_Zealand  {c |}{col 25}{res}{space 2}        0{col 37}{txt}  (omitted)
{space 16}Poland  {c |}{col 25}{res}{space 2}-.0945902{col 37}{space 2} .0150543{col 48}{space 1}   -6.28{col 57}{space 3}0.000{col 65}{space 4}-.1240976{col 78}{space 3}-.0650828
{txt}{space 17}Spain  {c |}{col 25}{res}{space 2}        0{col 37}{txt}  (omitted)
{space 16}Sweden  {c |}{col 25}{res}{space 2}   .06209{col 37}{space 2} .0174006{col 48}{space 1}    3.57{col 57}{space 3}0.000{col 65}{space 4} .0279836{col 78}{space 3} .0961965
{txt}{space 20}UK  {c |}{col 25}{res}{space 2}        0{col 37}{txt}  (omitted)
{space 20}US  {c |}{col 25}{res}{space 2}        0{col 37}{txt}  (omitted)
{space 23} {c |}
{space 18}_cons {c |}{col 25}{res}{space 2} .2059275{col 37}{space 2} .0389178{col 48}{space 1}    5.29{col 57}{space 3}0.000{col 65}{space 4} .1296459{col 78}{space 3} .2822091
{txt}{hline 24}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{help ivreg2##idtest:Underidentification test} (Kleibergen-Paap rk LM statistic):{res}{col 71}  14.941
{txt}{col 52}Chi-sq({res}8{txt}) P-val =  {res}{col 73}0.0603
{txt}{hline 78}
{help ivreg2##widtest:Weak identification test} (Cragg-Donald Wald F statistic):{res}{col 71}   1.835
{txt}                         (Kleibergen-Paap rk Wald F statistic):{res}{col 71}   1.867
{txt}Stock-Yogo weak ID test critical values:{res}{txt}{col 42} 5% maximal IV relative bias{res}{col 73} 20.25
{txt}{col 42}10% maximal IV relative bias{res}{col 73} 11.39
{txt}{col 42}20% maximal IV relative bias{res}{col 73}  6.69
{txt}{col 42}30% maximal IV relative bias{res}{col 73}  4.99
{txt}{col 42}10% maximal IV size{res}{col 73} 33.84
{txt}{col 42}15% maximal IV size{res}{col 73} 18.54
{txt}{col 42}20% maximal IV size{res}{col 73} 13.24
{txt}{col 42}25% maximal IV size{res}{col 73} 10.50
{txt}Source: Stock-Yogo (2005).  Reproduced by permission.
NB: Critical values are for Cragg-Donald F statistic and i.i.d. errors.
{hline 78}
{help ivreg2##overidtests:Hansen J statistic} (overidentification test of all instruments):{res}{col 71}   5.257
{txt}{col 52}Chi-sq({res}7{txt}) P-val =  {res}{col 73}0.6286
{txt}{hline 78}
Instrumented:{col 23}satis_head
Included instruments:{col 23}bad_econ_situation bad_health_situation thirties fourties
{col 23}fifties sixties seventies income2quartile income3quartile
{col 23}income4quartile incomenoanswer female highschool college
{col 23}noreligion christiannotcatholic catholic fulltimeworker
{col 23}parttimeworker unemployed selfemployed outofLF goodhealth
{col 23}white black latino asian whitecollar bluecollar
{col 23}serviceworker mv_incomenoanswer mv_highschool
{col 23}mv_noreligion mv_parttimeworker mv_selfemployed
{col 23}mv_goodhealth mv_white mv_whitecollar 2.country 3.country
{col 23}5.country 6.country 8.country 10.country
Excluded instruments:{col 23}TE2 TE4 TH2 TH4 TH4_TE2 TH4_TE4 TH2_TE2 TH2_TE4
Dropped collinear:{col 23}mv_thirties mv_fourties mv_fifties mv_sixties
{col 23}mv_seventies mv_income2quartile mv_income3quartile
{col 23}mv_income4quartile mv_female mv_college
{col 23}mv_christiannotcatholic mv_catholic mv_fulltimeworker
{col 23}mv_unemployed mv_outofLF mv_black mv_latino mv_asian
{col 23}mv_bluecollar mv_serviceworker 4.country 7.country
{col 23}9.country 11.country 12.country
{hline 78}
({res}est5{txt} stored)

{com}. 
. estimates store mod4
{txt}
{com}. estadd scalar fstat = e(widstat)

{txt}added scalar:
              e(fstat) =  {res}1.8667129
{txt}
{com}. estadd local CFE "X", replace

{txt}added macro:
                e(CFE) : "{res:X}"

{com}. estadd local Controls "X", replace

{txt}added macro:
           e(Controls) : "{res:X}"

{com}. estadd local IV "8 IVs"

{txt}added macro:
                 e(IV) : "{res:8 IVs}"

{com}. estadd local nothing nothing2 = " "

{txt}added macro:
            e(nothing) : "{res:nothing2 = " "}"

{com}. estadd scalar hausman = `temp_hausman'

{txt}added scalar:
            e(hausman) =  {res}1
{txt}
{com}. local coef4 = _b[satis_head]
{txt}
{com}. local se4 = _se[satis_head]
{txt}
{com}. get_mean

{txt}added scalar:
                 e(av) =  {res}.5
{txt}
{com}. 
. * Z-test
. local z_value = abs(`coef4'-`coef2')/sqrt(`se4'^2+`se2'^2)
{txt}
{com}. di `z_value'
{res}.03311978
{txt}
{com}. estadd scalar wald = (1-normal(`z_value'))*2

{txt}added scalar:
               e(wald) =  {res}.97357907
{txt}
{com}. 
. local labgen: variable label satis_dem
{txt}
{com}. 
. esttab mod1 mod2 mod3 mod4 using "3_output/2_OA/Tables/TableE14.tex", depvar keep(satis_head bad_econ_situation bad_health_situation ) ///
>         label replace ///
>         cells(b(star fmt(%15.3fc)) se(par fmt(%15.3fc))) interaction(" $\times$ ") ///
>         star(* 0.10 ** 0.05 *** 0.01) ///
>         stats(Controls CFE N av IV fstat hausman wald, layout(@ @ @ @ @ @ @ @) fmt(%15s %15s %15.0fc %9.3f %9.3f %9.3f %9.3f %9.3f %9.3f ) ///
>         labels("Individual controls" "Country FE" Observations "Outcome mean" "Instruments" "F-statistic" "Hausman test p-value" "Z-test p-value")) ///
>         collabels(none) mlabels(none) ///
>         mgroups("`labgen'" , pattern(1 0 0 0) ///
>         prefix(\multicolumn{c -(}@span{c )-}{c -(}c{c )-}{c -(}) suffix({c )-}) span erepeat(\cmidrule(lr){c -(}@span{c )-}))
{res}{txt}(output written to {browse  `"3_output/2_OA/Tables/TableE14.tex"'})

{com}.         
.         
. **# Table E.15: Impact on support for democracy, treatments mentioning government - 2SLS.       
. eststo clear
{txt}
{com}. /* E.15. Col.1. Initial coefficient: 16 instruments */
. eststo: ivreg2 democ ///
>         (satis_head = TH1_TE1 TH1_TE2 TH1_TE3 TH1_TE4 TH2_TE1 TH2_TE2 TH2_TE3 TH2_TE4 TH3_TE1 TH3_TE2 TH3_TE3 TH3_TE4 TH4_TE1 TH4_TE2 TH4_TE3), small robust 
{res}
{txt}IV (2SLS) estimation
{hline 20}

Estimates efficient for homoskedasticity only
Statistics robust to heteroskedasticity

{col 55}Number of obs = {res}   22537
{txt}{col 55}F(  1, 22535) = {res}    0.02
{txt}{col 55}Prob > F      = {res}  0.8762
{txt}Total (centered) SS     = {res} 1987.655944{txt}{col 55}Centered R2   = {res}  0.0022
{txt}Total (uncentered) SS   = {res}       20334{txt}{col 55}Uncentered R2 = {res}  0.9025
{txt}Residual SS             = {res} 1983.237445{txt}{col 55}Root MSE      = {res}   .2967

{txt}{hline 13}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 14}{c |}{col 26}    Robust
{col 1}       democ{col 14}{c |} Coefficient{col 26}  std. err.{col 38}      t{col 46}   P>|t|{col 54}     [95% con{col 67}f. interval]
{hline 13}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 2}satis_head {c |}{col 14}{res}{space 2} .0337519{col 26}{space 2}  .216718{col 37}{space 1}    0.16{col 46}{space 3}0.876{col 54}{space 4}-.3910303{col 67}{space 3} .4585342
{txt}{space 7}_cons {c |}{col 14}{res}{space 2} .8867837{col 26}{space 2} .0993297{col 37}{space 1}    8.93{col 46}{space 3}0.000{col 54}{space 4} .6920906{col 67}{space 3} 1.081477
{txt}{hline 13}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{help ivreg2##idtest:Underidentification test} (Kleibergen-Paap rk LM statistic):{res}{col 71}  19.879
{txt}{col 52}Chi-sq({res}15{txt}) P-val =  {res}{col 73}0.1766
{txt}{hline 78}
{help ivreg2##widtest:Weak identification test} (Cragg-Donald Wald F statistic):{res}{col 71}   1.320
{txt}                         (Kleibergen-Paap rk Wald F statistic):{res}{col 71}   1.330
{txt}Stock-Yogo weak ID test critical values:{res}{txt}{col 42} 5% maximal IV relative bias{res}{col 73} 21.23
{txt}{col 42}10% maximal IV relative bias{res}{col 73} 11.51
{txt}{col 42}20% maximal IV relative bias{res}{col 73}  6.42
{txt}{col 42}30% maximal IV relative bias{res}{col 73}  4.63
{txt}{col 42}10% maximal IV size{res}{col 73} 50.39
{txt}{col 42}15% maximal IV size{res}{col 73} 26.80
{txt}{col 42}20% maximal IV size{res}{col 73} 18.72
{txt}{col 42}25% maximal IV size{res}{col 73} 14.60
{txt}Source: Stock-Yogo (2005).  Reproduced by permission.
NB: Critical values are for Cragg-Donald F statistic and i.i.d. errors.
{hline 78}
{help ivreg2##overidtests:Hansen J statistic} (overidentification test of all instruments):{res}{col 71}  11.772
{txt}{col 52}Chi-sq({res}14{txt}) P-val =  {res}{col 73}0.6246
{txt}{hline 78}
Instrumented:{col 23}satis_head
Excluded instruments:{col 23}TH1_TE1 TH1_TE2 TH1_TE3 TH1_TE4 TH2_TE1 TH2_TE2 TH2_TE3
{col 23}TH2_TE4 TH3_TE1 TH3_TE2 TH3_TE3 TH3_TE4 TH4_TE1 TH4_TE2
{col 23}TH4_TE3
{hline 78}
({res}est1{txt} stored)

{com}. 
. estimates store mod1
{txt}
{com}. estadd scalar fstat = e(widstat)

{txt}added scalar:
              e(fstat) =  {res}1.3295479
{txt}
{com}. estadd local IV "16 IVs"

{txt}added macro:
                 e(IV) : "{res:16 IVs}"

{com}. estadd local nothing nothing2 = " "

{txt}added macro:
            e(nothing) : "{res:nothing2 = " "}"

{com}. get_mean

{txt}added scalar:
                 e(av) =  {res}.902
{txt}
{com}. local coef1 = _b[satis_head]
{txt}
{com}. local se1 = _se[satis_head]
{txt}
{com}. 
. ** for Hausman later 
. qui ivreg2 democ ///
>         (satis_head = TH1_TE1 TH1_TE2 TH1_TE3 TH1_TE4 TH2_TE1 TH2_TE2 TH2_TE3 TH2_TE4 TH3_TE1 TH3_TE2 TH3_TE3 TH3_TE4 TH4_TE1 TH4_TE2 TH4_TE3), small 
{txt}
{com}. estimates store mod1b
{txt}
{com}. 
. /* E.15. Col.2. Initial coefficient: 16 instruments + controls */
. eststo: ivreg2 democ ///
>         (satis_head = TH1_TE1 TH1_TE2 TH1_TE3 TH1_TE4 TH2_TE1 TH2_TE2 TH2_TE3 TH2_TE4 TH3_TE1 TH3_TE2 TH3_TE3 TH3_TE4 TH4_TE1 TH4_TE2 TH4_TE3) $controls $mv_controls i.country, small robust
{txt}Warning - collinearities detected
Vars dropped:{col 21}mv_thirties mv_fourties mv_fifties mv_sixties mv_seventies
{col 21}mv_income2quartile mv_income3quartile mv_income4quartile
{col 21}mv_female mv_college mv_christiannotcatholic mv_catholic
{col 21}mv_fulltimeworker mv_unemployed mv_outofLF mv_black
{col 21}mv_latino mv_asian mv_bluecollar mv_serviceworker 4.country
{col 21}7.country 9.country 11.country 12.country
{res}
{txt}IV (2SLS) estimation
{hline 20}

Estimates efficient for homoskedasticity only
Statistics robust to heteroskedasticity

{col 55}Number of obs = {res}   22537
{txt}{col 55}F( 43, 22493) = {res}   18.80
{txt}{col 55}Prob > F      = {res}  0.0000
{txt}Total (centered) SS     = {res} 1987.655944{txt}{col 55}Centered R2   = {res}  0.0392
{txt}Total (uncentered) SS   = {res}       20334{txt}{col 55}Uncentered R2 = {res}  0.9061
{txt}Residual SS             = {res} 1909.785059{txt}{col 55}Root MSE      = {res}   .2914

{txt}{hline 24}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 25}{c |}{col 37}    Robust
{col 1}                  democ{col 25}{c |} Coefficient{col 37}  std. err.{col 49}      t{col 57}   P>|t|{col 65}     [95% con{col 78}f. interval]
{hline 24}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 13}satis_head {c |}{col 25}{res}{space 2} .1023982{col 37}{space 2} .2083611{col 48}{space 1}    0.49{col 57}{space 3}0.623{col 65}{space 4} -.306004{col 78}{space 3} .5108004
{txt}{space 15}thirties {c |}{col 25}{res}{space 2} .0060911{col 37}{space 2} .0083963{col 48}{space 1}    0.73{col 57}{space 3}0.468{col 65}{space 4}-.0103662{col 78}{space 3} .0225484
{txt}{space 15}fourties {c |}{col 25}{res}{space 2} .0163058{col 37}{space 2}  .010126{col 48}{space 1}    1.61{col 57}{space 3}0.107{col 65}{space 4}-.0035418{col 78}{space 3} .0361535
{txt}{space 16}fifties {c |}{col 25}{res}{space 2} .0462606{col 37}{space 2}  .010371{col 48}{space 1}    4.46{col 57}{space 3}0.000{col 65}{space 4} .0259328{col 78}{space 3} .0665884
{txt}{space 16}sixties {c |}{col 25}{res}{space 2} .0689589{col 37}{space 2} .0105067{col 48}{space 1}    6.56{col 57}{space 3}0.000{col 65}{space 4}  .048365{col 78}{space 3} .0895528
{txt}{space 14}seventies {c |}{col 25}{res}{space 2} .0840891{col 37}{space 2} .0085161{col 48}{space 1}    9.87{col 57}{space 3}0.000{col 65}{space 4} .0673969{col 78}{space 3} .1007813
{txt}{space 8}income2quartile {c |}{col 25}{res}{space 2} .0281968{col 37}{space 2} .0071727{col 48}{space 1}    3.93{col 57}{space 3}0.000{col 65}{space 4} .0141378{col 78}{space 3} .0422558
{txt}{space 8}income3quartile {c |}{col 25}{res}{space 2} .0469364{col 37}{space 2} .0077142{col 48}{space 1}    6.08{col 57}{space 3}0.000{col 65}{space 4} .0318161{col 78}{space 3} .0620567
{txt}{space 8}income4quartile {c |}{col 25}{res}{space 2} .0509931{col 37}{space 2} .0103655{col 48}{space 1}    4.92{col 57}{space 3}0.000{col 65}{space 4}  .030676{col 78}{space 3} .0713102
{txt}{space 9}incomenoanswer {c |}{col 25}{res}{space 2} .0085015{col 37}{space 2} .0098195{col 48}{space 1}    0.87{col 57}{space 3}0.387{col 65}{space 4}-.0107453{col 78}{space 3} .0277484
{txt}{space 17}female {c |}{col 25}{res}{space 2}-.0101964{col 37}{space 2} .0045093{col 48}{space 1}   -2.26{col 57}{space 3}0.024{col 65}{space 4} -.019035{col 78}{space 3}-.0013578
{txt}{space 13}highschool {c |}{col 25}{res}{space 2} .0239526{col 37}{space 2} .0075669{col 48}{space 1}    3.17{col 57}{space 3}0.002{col 65}{space 4}  .009121{col 78}{space 3} .0387843
{txt}{space 16}college {c |}{col 25}{res}{space 2} .0670183{col 37}{space 2} .0066756{col 48}{space 1}   10.04{col 57}{space 3}0.000{col 65}{space 4} .0539336{col 78}{space 3}  .080103
{txt}{space 13}noreligion {c |}{col 25}{res}{space 2} .0394337{col 37}{space 2} .0116877{col 48}{space 1}    3.37{col 57}{space 3}0.001{col 65}{space 4} .0165249{col 78}{space 3} .0623424
{txt}{space 3}christiannotcatholic {c |}{col 25}{res}{space 2} .0293499{col 37}{space 2} .0172298{col 48}{space 1}    1.70{col 57}{space 3}0.088{col 65}{space 4}-.0044217{col 78}{space 3} .0631216
{txt}{space 15}catholic {c |}{col 25}{res}{space 2} .0354713{col 37}{space 2} .0109128{col 48}{space 1}    3.25{col 57}{space 3}0.001{col 65}{space 4} .0140814{col 78}{space 3} .0568612
{txt}{space 9}fulltimeworker {c |}{col 25}{res}{space 2} .0166043{col 37}{space 2}  .065966{col 48}{space 1}    0.25{col 57}{space 3}0.801{col 65}{space 4}-.1126938{col 78}{space 3} .1459023
{txt}{space 9}parttimeworker {c |}{col 25}{res}{space 2} .0424428{col 37}{space 2} .0677941{col 48}{space 1}    0.63{col 57}{space 3}0.531{col 65}{space 4}-.0904383{col 78}{space 3}  .175324
{txt}{space 13}unemployed {c |}{col 25}{res}{space 2}  .012916{col 37}{space 2} .0746332{col 48}{space 1}    0.17{col 57}{space 3}0.863{col 65}{space 4}-.1333702{col 78}{space 3} .1592022
{txt}{space 11}selfemployed {c |}{col 25}{res}{space 2} .0137822{col 37}{space 2} .0825596{col 48}{space 1}    0.17{col 57}{space 3}0.867{col 65}{space 4}-.1480404{col 78}{space 3} .1756047
{txt}{space 16}outofLF {c |}{col 25}{res}{space 2}  .029226{col 37}{space 2} .0687087{col 48}{space 1}    0.43{col 57}{space 3}0.671{col 65}{space 4}-.1054478{col 78}{space 3} .1638997
{txt}{space 13}goodhealth {c |}{col 25}{res}{space 2} .0274864{col 37}{space 2} .0113374{col 48}{space 1}    2.42{col 57}{space 3}0.015{col 65}{space 4} .0052643{col 78}{space 3} .0497084
{txt}{space 18}white {c |}{col 25}{res}{space 2} .0506319{col 37}{space 2} .0648562{col 48}{space 1}    0.78{col 57}{space 3}0.435{col 65}{space 4}-.0764907{col 78}{space 3} .1777545
{txt}{space 18}black {c |}{col 25}{res}{space 2}  .069515{col 37}{space 2} .0642737{col 48}{space 1}    1.08{col 57}{space 3}0.279{col 65}{space 4} -.056466{col 78}{space 3}  .195496
{txt}{space 17}latino {c |}{col 25}{res}{space 2} .0965484{col 37}{space 2} .0684786{col 48}{space 1}    1.41{col 57}{space 3}0.159{col 65}{space 4}-.0376744{col 78}{space 3} .2307712
{txt}{space 18}asian {c |}{col 25}{res}{space 2} .0816873{col 37}{space 2} .0690595{col 48}{space 1}    1.18{col 57}{space 3}0.237{col 65}{space 4}-.0536741{col 78}{space 3} .2170487
{txt}{space 12}whitecollar {c |}{col 25}{res}{space 2}-.0120083{col 37}{space 2} .0102888{col 48}{space 1}   -1.17{col 57}{space 3}0.243{col 65}{space 4}-.0321751{col 78}{space 3} .0081585
{txt}{space 13}bluecollar {c |}{col 25}{res}{space 2}-.0207131{col 37}{space 2} .0123368{col 48}{space 1}   -1.68{col 57}{space 3}0.093{col 65}{space 4}-.0448941{col 78}{space 3}  .003468
{txt}{space 10}serviceworker {c |}{col 25}{res}{space 2}-.0112827{col 37}{space 2} .0086907{col 48}{space 1}   -1.30{col 57}{space 3}0.194{col 65}{space 4}-.0283171{col 78}{space 3} .0057517
{txt}{space 12}mv_thirties {c |}{col 25}{res}{space 2}        0{col 37}{txt}  (omitted)
{space 12}mv_fourties {c |}{col 25}{res}{space 2}        0{col 37}{txt}  (omitted)
{space 13}mv_fifties {c |}{col 25}{res}{space 2}        0{col 37}{txt}  (omitted)
{space 13}mv_sixties {c |}{col 25}{res}{space 2}        0{col 37}{txt}  (omitted)
{space 11}mv_seventies {c |}{col 25}{res}{space 2}        0{col 37}{txt}  (omitted)
{space 5}mv_income2quartile {c |}{col 25}{res}{space 2}        0{col 37}{txt}  (omitted)
{space 5}mv_income3quartile {c |}{col 25}{res}{space 2}        0{col 37}{txt}  (omitted)
{space 5}mv_income4quartile {c |}{col 25}{res}{space 2}        0{col 37}{txt}  (omitted)
{space 6}mv_incomenoanswer {c |}{col 25}{res}{space 2} .0005538{col 37}{space 2} .0199732{col 48}{space 1}    0.03{col 57}{space 3}0.978{col 65}{space 4}-.0385949{col 78}{space 3} .0397026
{txt}{space 14}mv_female {c |}{col 25}{res}{space 2}        0{col 37}{txt}  (omitted)
{space 10}mv_highschool {c |}{col 25}{res}{space 2} .0251337{col 37}{space 2} .0175051{col 48}{space 1}    1.44{col 57}{space 3}0.151{col 65}{space 4}-.0091775{col 78}{space 3} .0594449
{txt}{space 13}mv_college {c |}{col 25}{res}{space 2}        0{col 37}{txt}  (omitted)
{space 10}mv_noreligion {c |}{col 25}{res}{space 2} .0317691{col 37}{space 2} .0266812{col 48}{space 1}    1.19{col 57}{space 3}0.234{col 65}{space 4} -.020528{col 78}{space 3} .0840662
{txt}mv_christiannotcatholic {c |}{col 25}{res}{space 2}        0{col 37}{txt}  (omitted)
{space 12}mv_catholic {c |}{col 25}{res}{space 2}        0{col 37}{txt}  (omitted)
{space 6}mv_fulltimeworker {c |}{col 25}{res}{space 2}        0{col 37}{txt}  (omitted)
{space 6}mv_parttimeworker {c |}{col 25}{res}{space 2}   .03905{col 37}{space 2} .0641943{col 48}{space 1}    0.61{col 57}{space 3}0.543{col 65}{space 4}-.0867752{col 78}{space 3} .1648753
{txt}{space 10}mv_unemployed {c |}{col 25}{res}{space 2}        0{col 37}{txt}  (omitted)
{space 8}mv_selfemployed {c |}{col 25}{res}{space 2} -.066599{col 37}{space 2} .0657347{col 48}{space 1}   -1.01{col 57}{space 3}0.311{col 65}{space 4}-.1954436{col 78}{space 3} .0622457
{txt}{space 13}mv_outofLF {c |}{col 25}{res}{space 2}        0{col 37}{txt}  (omitted)
{space 10}mv_goodhealth {c |}{col 25}{res}{space 2}-.1839394{col 37}{space 2}  .141094{col 48}{space 1}   -1.30{col 57}{space 3}0.192{col 65}{space 4}-.4604934{col 78}{space 3} .0926146
{txt}{space 15}mv_white {c |}{col 25}{res}{space 2} .1285453{col 37}{space 2} .0988659{col 48}{space 1}    1.30{col 57}{space 3}0.194{col 65}{space 4}-.0652387{col 78}{space 3} .3223293
{txt}{space 15}mv_black {c |}{col 25}{res}{space 2}        0{col 37}{txt}  (omitted)
{space 14}mv_latino {c |}{col 25}{res}{space 2}        0{col 37}{txt}  (omitted)
{space 15}mv_asian {c |}{col 25}{res}{space 2}        0{col 37}{txt}  (omitted)
{space 9}mv_whitecollar {c |}{col 25}{res}{space 2}-.0705549{col 37}{space 2} .0668946{col 48}{space 1}   -1.05{col 57}{space 3}0.292{col 65}{space 4}-.2016729{col 78}{space 3} .0605631
{txt}{space 10}mv_bluecollar {c |}{col 25}{res}{space 2}        0{col 37}{txt}  (omitted)
{space 7}mv_serviceworker {c |}{col 25}{res}{space 2}        0{col 37}{txt}  (omitted)
{space 23} {c |}
{space 16}country {c |}
{space 15}Austria  {c |}{col 25}{res}{space 2} .1204074{col 37}{space 2}  .124201{col 48}{space 1}    0.97{col 57}{space 3}0.332{col 65}{space 4}-.1230353{col 78}{space 3} .3638501
{txt}{space 16}Brazil  {c |}{col 25}{res}{space 2}-.0698866{col 37}{space 2} .0217084{col 48}{space 1}   -3.22{col 57}{space 3}0.001{col 65}{space 4}-.1124365{col 78}{space 3}-.0273366
{txt}{space 16}France  {c |}{col 25}{res}{space 2}        0{col 37}{txt}  (omitted)
{space 15}Germany  {c |}{col 25}{res}{space 2}  .062125{col 37}{space 2} .1312827{col 48}{space 1}    0.47{col 57}{space 3}0.636{col 65}{space 4}-.1951981{col 78}{space 3} .3194482
{txt}{space 17}Italy  {c |}{col 25}{res}{space 2} .1044462{col 37}{space 2} .1223026{col 48}{space 1}    0.85{col 57}{space 3}0.393{col 65}{space 4}-.1352754{col 78}{space 3} .3441678
{txt}{space 11}New_Zealand  {c |}{col 25}{res}{space 2}        0{col 37}{txt}  (omitted)
{space 16}Poland  {c |}{col 25}{res}{space 2} -.105539{col 37}{space 2} .0233474{col 48}{space 1}   -4.52{col 57}{space 3}0.000{col 65}{space 4}-.1513015{col 78}{space 3}-.0597766
{txt}{space 17}Spain  {c |}{col 25}{res}{space 2}        0{col 37}{txt}  (omitted)
{space 16}Sweden  {c |}{col 25}{res}{space 2} -.066631{col 37}{space 2} .0239689{col 48}{space 1}   -2.78{col 57}{space 3}0.005{col 65}{space 4}-.1136117{col 78}{space 3}-.0196502
{txt}{space 20}UK  {c |}{col 25}{res}{space 2}        0{col 37}{txt}  (omitted)
{space 20}US  {c |}{col 25}{res}{space 2}        0{col 37}{txt}  (omitted)
{space 23} {c |}
{space 18}_cons {c |}{col 25}{res}{space 2} .6636018{col 37}{space 2} .0633164{col 48}{space 1}   10.48{col 57}{space 3}0.000{col 65}{space 4} .5394972{col 78}{space 3} .7877065
{txt}{hline 24}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{help ivreg2##idtest:Underidentification test} (Kleibergen-Paap rk LM statistic):{res}{col 71}  23.663
{txt}{col 52}Chi-sq({res}15{txt}) P-val =  {res}{col 73}0.0710
{txt}{hline 78}
{help ivreg2##widtest:Weak identification test} (Cragg-Donald Wald F statistic):{res}{col 71}   1.551
{txt}                         (Kleibergen-Paap rk Wald F statistic):{res}{col 71}   1.580
{txt}Stock-Yogo weak ID test critical values:{res}{txt}{col 42} 5% maximal IV relative bias{res}{col 73} 21.23
{txt}{col 42}10% maximal IV relative bias{res}{col 73} 11.51
{txt}{col 42}20% maximal IV relative bias{res}{col 73}  6.42
{txt}{col 42}30% maximal IV relative bias{res}{col 73}  4.63
{txt}{col 42}10% maximal IV size{res}{col 73} 50.39
{txt}{col 42}15% maximal IV size{res}{col 73} 26.80
{txt}{col 42}20% maximal IV size{res}{col 73} 18.72
{txt}{col 42}25% maximal IV size{res}{col 73} 14.60
{txt}Source: Stock-Yogo (2005).  Reproduced by permission.
NB: Critical values are for Cragg-Donald F statistic and i.i.d. errors.
{hline 78}
{help ivreg2##overidtests:Hansen J statistic} (overidentification test of all instruments):{res}{col 71}  13.744
{txt}{col 52}Chi-sq({res}14{txt}) P-val =  {res}{col 73}0.4690
{txt}{hline 78}
Instrumented:{col 23}satis_head
Included instruments:{col 23}thirties fourties fifties sixties seventies
{col 23}income2quartile income3quartile income4quartile
{col 23}incomenoanswer female highschool college noreligion
{col 23}christiannotcatholic catholic fulltimeworker
{col 23}parttimeworker unemployed selfemployed outofLF goodhealth
{col 23}white black latino asian whitecollar bluecollar
{col 23}serviceworker mv_incomenoanswer mv_highschool
{col 23}mv_noreligion mv_parttimeworker mv_selfemployed
{col 23}mv_goodhealth mv_white mv_whitecollar 2.country 3.country
{col 23}5.country 6.country 8.country 10.country
Excluded instruments:{col 23}TH1_TE1 TH1_TE2 TH1_TE3 TH1_TE4 TH2_TE1 TH2_TE2 TH2_TE3
{col 23}TH2_TE4 TH3_TE1 TH3_TE2 TH3_TE3 TH3_TE4 TH4_TE1 TH4_TE2
{col 23}TH4_TE3
Dropped collinear:{col 23}mv_thirties mv_fourties mv_fifties mv_sixties
{col 23}mv_seventies mv_income2quartile mv_income3quartile
{col 23}mv_income4quartile mv_female mv_college
{col 23}mv_christiannotcatholic mv_catholic mv_fulltimeworker
{col 23}mv_unemployed mv_outofLF mv_black mv_latino mv_asian
{col 23}mv_bluecollar mv_serviceworker 4.country 7.country
{col 23}9.country 11.country 12.country
{hline 78}
({res}est2{txt} stored)

{com}. 
. estimates store mod2
{txt}
{com}. estadd scalar fstat = e(widstat)

{txt}added scalar:
              e(fstat) =  {res}1.5801279
{txt}
{com}. estadd local CFE "X", replace

{txt}added macro:
                e(CFE) : "{res:X}"

{com}. estadd local Controls "X", replace

{txt}added macro:
           e(Controls) : "{res:X}"

{com}. estadd local IV "16 IVs"

{txt}added macro:
                 e(IV) : "{res:16 IVs}"

{com}. estadd local nothing nothing2 = " "

{txt}added macro:
            e(nothing) : "{res:nothing2 = " "}"

{com}. local coef2 = _b[satis_head]
{txt}
{com}. local se2 = _se[satis_head]
{txt}
{com}. get_mean

{txt}added scalar:
                 e(av) =  {res}.902
{txt}
{com}. 
. * for Hausman later                                                                                                                     
. eststo: ivreg2 democ ///
>         (satis_head = TH1_TE1 TH1_TE2 TH1_TE3 TH1_TE4 TH2_TE1 TH2_TE2 TH2_TE3 TH2_TE4 TH3_TE1 TH3_TE2 TH3_TE3 TH3_TE4 TH4_TE1 TH4_TE2 TH4_TE3) $controls $mv_controls i.country, small 
{txt}Warning - collinearities detected
Vars dropped:{col 21}mv_thirties mv_fourties mv_fifties mv_sixties mv_seventies
{col 21}mv_income2quartile mv_income3quartile mv_income4quartile
{col 21}mv_female mv_college mv_christiannotcatholic mv_catholic
{col 21}mv_fulltimeworker mv_unemployed mv_outofLF mv_black
{col 21}mv_latino mv_asian mv_bluecollar mv_serviceworker 4.country
{col 21}7.country 9.country 11.country 12.country
{res}
{txt}IV (2SLS) estimation
{hline 20}

Estimates efficient for homoskedasticity only
Statistics consistent for homoskedasticity only

{col 55}Number of obs = {res}   22537
{txt}{col 55}F( 43, 22493) = {res}   23.27
{txt}{col 55}Prob > F      = {res}  0.0000
{txt}Total (centered) SS     = {res} 1987.655944{txt}{col 55}Centered R2   = {res}  0.0392
{txt}Total (uncentered) SS   = {res}       20334{txt}{col 55}Uncentered R2 = {res}  0.9061
{txt}Residual SS             = {res} 1909.785059{txt}{col 55}Root MSE      = {res}   .2914

{txt}{hline 24}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 1}                  democ{col 25}{c |} Coefficient{col 37}  Std. err.{col 49}      t{col 57}   P>|t|{col 65}     [95% con{col 78}f. interval]
{hline 24}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 13}satis_head {c |}{col 25}{res}{space 2} .1023982{col 37}{space 2} .2048628{col 48}{space 1}    0.50{col 57}{space 3}0.617{col 65}{space 4}-.2991471{col 78}{space 3} .5039434
{txt}{space 15}thirties {c |}{col 25}{res}{space 2} .0060911{col 37}{space 2} .0075078{col 48}{space 1}    0.81{col 57}{space 3}0.417{col 65}{space 4}-.0086246{col 78}{space 3} .0208068
{txt}{space 15}fourties {c |}{col 25}{res}{space 2} .0163058{col 37}{space 2} .0094679{col 48}{space 1}    1.72{col 57}{space 3}0.085{col 65}{space 4}-.0022518{col 78}{space 3} .0348635
{txt}{space 16}fifties {c |}{col 25}{res}{space 2} .0462606{col 37}{space 2} .0099301{col 48}{space 1}    4.66{col 57}{space 3}0.000{col 65}{space 4} .0267968{col 78}{space 3} .0657244
{txt}{space 16}sixties {c |}{col 25}{res}{space 2} .0689589{col 37}{space 2} .0102352{col 48}{space 1}    6.74{col 57}{space 3}0.000{col 65}{space 4} .0488971{col 78}{space 3} .0890206
{txt}{space 14}seventies {c |}{col 25}{res}{space 2} .0840891{col 37}{space 2} .0089704{col 48}{space 1}    9.37{col 57}{space 3}0.000{col 65}{space 4} .0665065{col 78}{space 3} .1016718
{txt}{space 8}income2quartile {c |}{col 25}{res}{space 2} .0281968{col 37}{space 2} .0067382{col 48}{space 1}    4.18{col 57}{space 3}0.000{col 65}{space 4} .0149895{col 78}{space 3} .0414041
{txt}{space 8}income3quartile {c |}{col 25}{res}{space 2} .0469364{col 37}{space 2} .0075467{col 48}{space 1}    6.22{col 57}{space 3}0.000{col 65}{space 4} .0321443{col 78}{space 3} .0617286
{txt}{space 8}income4quartile {c |}{col 25}{res}{space 2} .0509931{col 37}{space 2} .0104298{col 48}{space 1}    4.89{col 57}{space 3}0.000{col 65}{space 4}   .03055{col 78}{space 3} .0714362
{txt}{space 9}incomenoanswer {c |}{col 25}{res}{space 2} .0085015{col 37}{space 2} .0094529{col 48}{space 1}    0.90{col 57}{space 3}0.368{col 65}{space 4}-.0100267{col 78}{space 3} .0270298
{txt}{space 17}female {c |}{col 25}{res}{space 2}-.0101964{col 37}{space 2} .0045282{col 48}{space 1}   -2.25{col 57}{space 3}0.024{col 65}{space 4} -.019072{col 78}{space 3}-.0013208
{txt}{space 13}highschool {c |}{col 25}{res}{space 2} .0239526{col 37}{space 2} .0069169{col 48}{space 1}    3.46{col 57}{space 3}0.001{col 65}{space 4}  .010395{col 78}{space 3} .0375103
{txt}{space 16}college {c |}{col 25}{res}{space 2} .0670183{col 37}{space 2} .0062875{col 48}{space 1}   10.66{col 57}{space 3}0.000{col 65}{space 4} .0546944{col 78}{space 3} .0793422
{txt}{space 13}noreligion {c |}{col 25}{res}{space 2} .0394337{col 37}{space 2} .0104516{col 48}{space 1}    3.77{col 57}{space 3}0.000{col 65}{space 4} .0189479{col 78}{space 3} .0599195
{txt}{space 3}christiannotcatholic {c |}{col 25}{res}{space 2} .0293499{col 37}{space 2} .0161293{col 48}{space 1}    1.82{col 57}{space 3}0.069{col 65}{space 4}-.0022646{col 78}{space 3} .0609645
{txt}{space 15}catholic {c |}{col 25}{res}{space 2} .0354713{col 37}{space 2} .0095696{col 48}{space 1}    3.71{col 57}{space 3}0.000{col 65}{space 4} .0167143{col 78}{space 3} .0542283
{txt}{space 9}fulltimeworker {c |}{col 25}{res}{space 2} .0166043{col 37}{space 2} .0648411{col 48}{space 1}    0.26{col 57}{space 3}0.798{col 65}{space 4}-.1104888{col 78}{space 3} .1436973
{txt}{space 9}parttimeworker {c |}{col 25}{res}{space 2} .0424428{col 37}{space 2} .0671376{col 48}{space 1}    0.63{col 57}{space 3}0.527{col 65}{space 4}-.0891515{col 78}{space 3} .1740372
{txt}{space 13}unemployed {c |}{col 25}{res}{space 2}  .012916{col 37}{space 2}  .072631{col 48}{space 1}    0.18{col 57}{space 3}0.859{col 65}{space 4}-.1294458{col 78}{space 3} .1552777
{txt}{space 11}selfemployed {c |}{col 25}{res}{space 2} .0137822{col 37}{space 2} .0835957{col 48}{space 1}    0.16{col 57}{space 3}0.869{col 65}{space 4}-.1500712{col 78}{space 3} .1776356
{txt}{space 16}outofLF {c |}{col 25}{res}{space 2}  .029226{col 37}{space 2}  .067401{col 48}{space 1}    0.43{col 57}{space 3}0.665{col 65}{space 4}-.1028848{col 78}{space 3} .1613367
{txt}{space 13}goodhealth {c |}{col 25}{res}{space 2} .0274864{col 37}{space 2} .0110638{col 48}{space 1}    2.48{col 57}{space 3}0.013{col 65}{space 4} .0058006{col 78}{space 3} .0491721
{txt}{space 18}white {c |}{col 25}{res}{space 2} .0506319{col 37}{space 2} .0472989{col 48}{space 1}    1.07{col 57}{space 3}0.284{col 65}{space 4}-.0420773{col 78}{space 3} .1433411
{txt}{space 18}black {c |}{col 25}{res}{space 2}  .069515{col 37}{space 2} .0429435{col 48}{space 1}    1.62{col 57}{space 3}0.106{col 65}{space 4}-.0146572{col 78}{space 3} .1536872
{txt}{space 17}latino {c |}{col 25}{res}{space 2} .0965484{col 37}{space 2} .0474937{col 48}{space 1}    2.03{col 57}{space 3}0.042{col 65}{space 4} .0034575{col 78}{space 3} .1896393
{txt}{space 18}asian {c |}{col 25}{res}{space 2} .0816873{col 37}{space 2}  .049178{col 48}{space 1}    1.66{col 57}{space 3}0.097{col 65}{space 4}-.0147049{col 78}{space 3} .1780795
{txt}{space 12}whitecollar {c |}{col 25}{res}{space 2}-.0120083{col 37}{space 2} .0106022{col 48}{space 1}   -1.13{col 57}{space 3}0.257{col 65}{space 4}-.0327894{col 78}{space 3} .0087727
{txt}{space 13}bluecollar {c |}{col 25}{res}{space 2}-.0207131{col 37}{space 2} .0114184{col 48}{space 1}   -1.81{col 57}{space 3}0.070{col 65}{space 4}-.0430938{col 78}{space 3} .0016677
{txt}{space 10}serviceworker {c |}{col 25}{res}{space 2}-.0112827{col 37}{space 2} .0085893{col 48}{space 1}   -1.31{col 57}{space 3}0.189{col 65}{space 4}-.0281183{col 78}{space 3} .0055529
{txt}{space 12}mv_thirties {c |}{col 25}{res}{space 2}        0{col 37}{txt}  (omitted)
{space 12}mv_fourties {c |}{col 25}{res}{space 2}        0{col 37}{txt}  (omitted)
{space 13}mv_fifties {c |}{col 25}{res}{space 2}        0{col 37}{txt}  (omitted)
{space 13}mv_sixties {c |}{col 25}{res}{space 2}        0{col 37}{txt}  (omitted)
{space 11}mv_seventies {c |}{col 25}{res}{space 2}        0{col 37}{txt}  (omitted)
{space 5}mv_income2quartile {c |}{col 25}{res}{space 2}        0{col 37}{txt}  (omitted)
{space 5}mv_income3quartile {c |}{col 25}{res}{space 2}        0{col 37}{txt}  (omitted)
{space 5}mv_income4quartile {c |}{col 25}{res}{space 2}        0{col 37}{txt}  (omitted)
{space 6}mv_incomenoanswer {c |}{col 25}{res}{space 2} .0005538{col 37}{space 2} .0202561{col 48}{space 1}    0.03{col 57}{space 3}0.978{col 65}{space 4}-.0391495{col 78}{space 3} .0402571
{txt}{space 14}mv_female {c |}{col 25}{res}{space 2}        0{col 37}{txt}  (omitted)
{space 10}mv_highschool {c |}{col 25}{res}{space 2} .0251337{col 37}{space 2} .0164781{col 48}{space 1}    1.53{col 57}{space 3}0.127{col 65}{space 4}-.0071646{col 78}{space 3} .0574319
{txt}{space 13}mv_college {c |}{col 25}{res}{space 2}        0{col 37}{txt}  (omitted)
{space 10}mv_noreligion {c |}{col 25}{res}{space 2} .0317691{col 37}{space 2} .0287099{col 48}{space 1}    1.11{col 57}{space 3}0.268{col 65}{space 4}-.0245043{col 78}{space 3} .0880425
{txt}mv_christiannotcatholic {c |}{col 25}{res}{space 2}        0{col 37}{txt}  (omitted)
{space 12}mv_catholic {c |}{col 25}{res}{space 2}        0{col 37}{txt}  (omitted)
{space 6}mv_fulltimeworker {c |}{col 25}{res}{space 2}        0{col 37}{txt}  (omitted)
{space 6}mv_parttimeworker {c |}{col 25}{res}{space 2}   .03905{col 37}{space 2} .0620527{col 48}{space 1}    0.63{col 57}{space 3}0.529{col 65}{space 4}-.0825775{col 78}{space 3} .1606776
{txt}{space 10}mv_unemployed {c |}{col 25}{res}{space 2}        0{col 37}{txt}  (omitted)
{space 8}mv_selfemployed {c |}{col 25}{res}{space 2} -.066599{col 37}{space 2} .0639458{col 48}{space 1}   -1.04{col 57}{space 3}0.298{col 65}{space 4}-.1919371{col 78}{space 3} .0587392
{txt}{space 13}mv_outofLF {c |}{col 25}{res}{space 2}        0{col 37}{txt}  (omitted)
{space 10}mv_goodhealth {c |}{col 25}{res}{space 2}-.1839394{col 37}{space 2} .0956128{col 48}{space 1}   -1.92{col 57}{space 3}0.054{col 65}{space 4}-.3713472{col 78}{space 3} .0034684
{txt}{space 15}mv_white {c |}{col 25}{res}{space 2} .1285453{col 37}{space 2} .0873043{col 48}{space 1}    1.47{col 57}{space 3}0.141{col 65}{space 4}-.0425772{col 78}{space 3} .2996678
{txt}{space 15}mv_black {c |}{col 25}{res}{space 2}        0{col 37}{txt}  (omitted)
{space 14}mv_latino {c |}{col 25}{res}{space 2}        0{col 37}{txt}  (omitted)
{space 15}mv_asian {c |}{col 25}{res}{space 2}        0{col 37}{txt}  (omitted)
{space 9}mv_whitecollar {c |}{col 25}{res}{space 2}-.0705549{col 37}{space 2} .0655439{col 48}{space 1}   -1.08{col 57}{space 3}0.282{col 65}{space 4}-.1990256{col 78}{space 3} .0579158
{txt}{space 10}mv_bluecollar {c |}{col 25}{res}{space 2}        0{col 37}{txt}  (omitted)
{space 7}mv_serviceworker {c |}{col 25}{res}{space 2}        0{col 37}{txt}  (omitted)
{space 23} {c |}
{space 16}country {c |}
{space 15}Austria  {c |}{col 25}{res}{space 2} .1204074{col 37}{space 2} .1144019{col 48}{space 1}    1.05{col 57}{space 3}0.293{col 65}{space 4}-.1038283{col 78}{space 3} .3446431
{txt}{space 16}Brazil  {c |}{col 25}{res}{space 2}-.0698866{col 37}{space 2} .0208531{col 48}{space 1}   -3.35{col 57}{space 3}0.001{col 65}{space 4}-.1107601{col 78}{space 3} -.029013
{txt}{space 16}France  {c |}{col 25}{res}{space 2}        0{col 37}{txt}  (omitted)
{space 15}Germany  {c |}{col 25}{res}{space 2}  .062125{col 37}{space 2} .1216058{col 48}{space 1}    0.51{col 57}{space 3}0.609{col 65}{space 4}-.1762308{col 78}{space 3} .3004808
{txt}{space 17}Italy  {c |}{col 25}{res}{space 2} .1044462{col 37}{space 2} .1130356{col 48}{space 1}    0.92{col 57}{space 3}0.355{col 65}{space 4}-.1171115{col 78}{space 3} .3260039
{txt}{space 11}New_Zealand  {c |}{col 25}{res}{space 2}        0{col 37}{txt}  (omitted)
{space 16}Poland  {c |}{col 25}{res}{space 2} -.105539{col 37}{space 2} .0220374{col 48}{space 1}   -4.79{col 57}{space 3}0.000{col 65}{space 4}-.1487339{col 78}{space 3}-.0623441
{txt}{space 17}Spain  {c |}{col 25}{res}{space 2}        0{col 37}{txt}  (omitted)
{space 16}Sweden  {c |}{col 25}{res}{space 2} -.066631{col 37}{space 2} .0237499{col 48}{space 1}   -2.81{col 57}{space 3}0.005{col 65}{space 4}-.1131824{col 78}{space 3}-.0200795
{txt}{space 20}UK  {c |}{col 25}{res}{space 2}        0{col 37}{txt}  (omitted)
{space 20}US  {c |}{col 25}{res}{space 2}        0{col 37}{txt}  (omitted)
{space 23} {c |}
{space 18}_cons {c |}{col 25}{res}{space 2} .6636018{col 37}{space 2} .0454322{col 48}{space 1}   14.61{col 57}{space 3}0.000{col 65}{space 4} .5745516{col 78}{space 3} .7526521
{txt}{hline 24}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{help ivreg2##idtest:Underidentification test} (Anderson canon. corr. LM statistic):{res}{col 71}  23.294
{txt}{col 52}Chi-sq({res}15{txt}) P-val =  {res}{col 73}0.0781
{txt}{hline 78}
{help ivreg2##widtest:Weak identification test} (Cragg-Donald Wald F statistic):{res}{col 71}   1.551
{txt}Stock-Yogo weak ID test critical values:{res}{txt}{col 42} 5% maximal IV relative bias{res}{col 73} 21.23
{txt}{col 42}10% maximal IV relative bias{res}{col 73} 11.51
{txt}{col 42}20% maximal IV relative bias{res}{col 73}  6.42
{txt}{col 42}30% maximal IV relative bias{res}{col 73}  4.63
{txt}{col 42}10% maximal IV size{res}{col 73} 50.39
{txt}{col 42}15% maximal IV size{res}{col 73} 26.80
{txt}{col 42}20% maximal IV size{res}{col 73} 18.72
{txt}{col 42}25% maximal IV size{res}{col 73} 14.60
{txt}Source: Stock-Yogo (2005).  Reproduced by permission.
{hline 78}
{help ivreg2##overidtests:Sargan statistic} (overidentification test of all instruments):{res}{col 71}  14.093
{txt}{col 52}Chi-sq({res}14{txt}) P-val =  {res}{col 73}0.4428
{txt}{hline 78}
Instrumented:{col 23}satis_head
Included instruments:{col 23}thirties fourties fifties sixties seventies
{col 23}income2quartile income3quartile income4quartile
{col 23}incomenoanswer female highschool college noreligion
{col 23}christiannotcatholic catholic fulltimeworker
{col 23}parttimeworker unemployed selfemployed outofLF goodhealth
{col 23}white black latino asian whitecollar bluecollar
{col 23}serviceworker mv_incomenoanswer mv_highschool
{col 23}mv_noreligion mv_parttimeworker mv_selfemployed
{col 23}mv_goodhealth mv_white mv_whitecollar 2.country 3.country
{col 23}5.country 6.country 8.country 10.country
Excluded instruments:{col 23}TH1_TE1 TH1_TE2 TH1_TE3 TH1_TE4 TH2_TE1 TH2_TE2 TH2_TE3
{col 23}TH2_TE4 TH3_TE1 TH3_TE2 TH3_TE3 TH3_TE4 TH4_TE1 TH4_TE2
{col 23}TH4_TE3
Dropped collinear:{col 23}mv_thirties mv_fourties mv_fifties mv_sixties
{col 23}mv_seventies mv_income2quartile mv_income3quartile
{col 23}mv_income4quartile mv_female mv_college
{col 23}mv_christiannotcatholic mv_catholic mv_fulltimeworker
{col 23}mv_unemployed mv_outofLF mv_black mv_latino mv_asian
{col 23}mv_bluecollar mv_serviceworker 4.country 7.country
{col 23}9.country 11.country 12.country
{hline 78}
({res}est3{txt} stored)

{com}. estimates store mod2b
{txt}
{com}. 
. * Hausman test for the following regression 
. qui ivreg2 democ ///
>         (satis_head = TE2 TE4 TH2 TH4 TH4_TE2 TH4_TE4 TH2_TE2 TH2_TE4) bad_econ_situation bad_health_situation, small
{txt}
{com}. estimates store mod3b
{txt}
{com}. hausman mod3b mod1b

{txt}{col 18}{hline 4} Coefficients {hline 4}
{col 14}{c |}{col 21}(b){col 34}(B){col 49}(b-B){col 59}sqrt(diag(V_b-V_B))
{col 14}{c |}{col 17}   mod3b    {col 30}   mod1b    {col 46}Difference{col 63}Std. err.
{hline 13}{c +}{hline 64}
{space 2}satis_head {c |}{res}{col 18} .0537574{col 31} .0337519{col 47} .0200055{col 63} .1679628
{txt}{hline 13}{c BT}{hline 64}
{ralign 78:b = Consistent under H0 and Ha; obtained from {bf:ivreg2}.}
{ralign 78:B = Inconsistent under Ha, efficient under H0; obtained from {bf:ivreg2}.}

Test of H0: Difference in coefficients not systematic

{ralign 11:chi2({res:1})} = (b-B)'[(V_b-V_B)^(-1)](b-B)
{ralign 11:} = {res:{ralign 6:0.01}}
{ralign 11:Prob > chi2} = {res:{ralign 6:0.9052}}

{com}. local temp_hausman = r(p)
{txt}
{com}. 
. /* E.15. Col.3. 8 instruments */
. eststo: ivreg2 democ ///
>         (satis_head = TE2 TE4 TH2 TH4 TH4_TE2 TH4_TE4 TH2_TE2 TH2_TE4) bad_econ_situation bad_health_situation, small robust
{res}
{txt}IV (2SLS) estimation
{hline 20}

Estimates efficient for homoskedasticity only
Statistics robust to heteroskedasticity

{col 55}Number of obs = {res}   22537
{txt}{col 55}F(  3, 22533) = {res}    0.09
{txt}{col 55}Prob > F      = {res}  0.9682
{txt}Total (centered) SS     = {res} 1987.655944{txt}{col 55}Centered R2   = {res}  0.0023
{txt}Total (uncentered) SS   = {res}       20334{txt}{col 55}Uncentered R2 = {res}  0.9025
{txt}Residual SS             = {res} 1982.998648{txt}{col 55}Root MSE      = {res}   .2967

{txt}{hline 21}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 22}{c |}{col 34}    Robust
{col 1}               democ{col 22}{c |} Coefficient{col 34}  std. err.{col 46}      t{col 54}   P>|t|{col 62}     [95% con{col 75}f. interval]
{hline 21}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 10}satis_head {c |}{col 22}{res}{space 2} .0537574{col 34}{space 2} .2740795{col 45}{space 1}    0.20{col 54}{space 3}0.845{col 62}{space 4}-.4834574{col 75}{space 3} .5909721
{txt}{space 2}bad_econ_situation {c |}{col 22}{res}{space 2} -.001265{col 34}{space 2} .0042824{col 45}{space 1}   -0.30{col 54}{space 3}0.768{col 62}{space 4}-.0096588{col 75}{space 3} .0071287
{txt}bad_health_situation {c |}{col 22}{res}{space 2}-.0004916{col 34}{space 2} .0045403{col 45}{space 1}   -0.11{col 54}{space 3}0.914{col 62}{space 4}-.0093909{col 75}{space 3} .0084077
{txt}{space 15}_cons {c |}{col 22}{res}{space 2} .8784972{col 34}{space 2} .1275876{col 45}{space 1}    6.89{col 54}{space 3}0.000{col 62}{space 4} .6284168{col 75}{space 3} 1.128578
{txt}{hline 21}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{help ivreg2##idtest:Underidentification test} (Kleibergen-Paap rk LM statistic):{res}{col 71}  12.198
{txt}{col 52}Chi-sq({res}8{txt}) P-val =  {res}{col 73}0.1426
{txt}{hline 78}
{help ivreg2##widtest:Weak identification test} (Cragg-Donald Wald F statistic):{res}{col 71}   1.522
{txt}                         (Kleibergen-Paap rk Wald F statistic):{res}{col 71}   1.527
{txt}Stock-Yogo weak ID test critical values:{res}{txt}{col 42} 5% maximal IV relative bias{res}{col 73} 20.25
{txt}{col 42}10% maximal IV relative bias{res}{col 73} 11.39
{txt}{col 42}20% maximal IV relative bias{res}{col 73}  6.69
{txt}{col 42}30% maximal IV relative bias{res}{col 73}  4.99
{txt}{col 42}10% maximal IV size{res}{col 73} 33.84
{txt}{col 42}15% maximal IV size{res}{col 73} 18.54
{txt}{col 42}20% maximal IV size{res}{col 73} 13.24
{txt}{col 42}25% maximal IV size{res}{col 73} 10.50
{txt}Source: Stock-Yogo (2005).  Reproduced by permission.
NB: Critical values are for Cragg-Donald F statistic and i.i.d. errors.
{hline 78}
{help ivreg2##overidtests:Hansen J statistic} (overidentification test of all instruments):{res}{col 71}   7.016
{txt}{col 52}Chi-sq({res}7{txt}) P-val =  {res}{col 73}0.4272
{txt}{hline 78}
Instrumented:{col 23}satis_head
Included instruments:{col 23}bad_econ_situation bad_health_situation
Excluded instruments:{col 23}TE2 TE4 TH2 TH4 TH4_TE2 TH4_TE4 TH2_TE2 TH2_TE4
{hline 78}
({res}est4{txt} stored)

{com}. 
. estimates store mod3
{txt}
{com}. estadd scalar fstat = e(widstat)

{txt}added scalar:
              e(fstat) =  {res}1.5266041
{txt}
{com}. estadd local IV "8 IVs"

{txt}added macro:
                 e(IV) : "{res:8 IVs}"

{com}. estadd local nothing nothing2 = " "

{txt}added macro:
            e(nothing) : "{res:nothing2 = " "}"

{com}. estadd scalar hausman = `temp_hausman'

{txt}added scalar:
            e(hausman) =  {res}.90519102
{txt}
{com}. local coef3 = _b[satis_head]
{txt}
{com}. local se3 = _se[satis_head]
{txt}
{com}. get_mean

{txt}added scalar:
                 e(av) =  {res}.902
{txt}
{com}. 
. * Z-test 
. local z_value = abs(`coef3'-`coef1')/sqrt(`se3'^2+`se1'^2)
{txt}
{com}. di (1-normal(`z_value'))*2
{res}.9543419
{txt}
{com}. estadd scalar wald = (1-normal(`z_value'))*2

{txt}added scalar:
               e(wald) =  {res}.9543419
{txt}
{com}. 
. * Hausman test for following regression
. qui ivreg2 democ ///
>         (satis_head = TE2 TE4 TH2 TH4 TH4_TE2 TH4_TE4 TH2_TE2 TH2_TE4) bad_econ_situation bad_health_situation $controls $mv_controls i.country, small 
{txt}
{com}. estimates store mod4b
{txt}
{com}. hausman mod4b mod2b

{txt}{p 0 8}Note: the rank of the differenced variance matrix ({result:39}) does not equal the number of coefficients being tested ({result:43}); be sure this is what you expect, or there may be problems computing the test.  Examine the output of your estimators for anything unexpected and possibly consider scaling your variables so that the coefficients are on a similar scale.

{col 18}{hline 4} Coefficients {hline 4}
{col 14}{c |}{col 21}(b){col 34}(B){col 49}(b-B){col 59}sqrt(diag(V_b-V_B))
{col 14}{c |}{col 17}   mod4b    {col 30}   mod2b    {col 46}Difference{col 63}Std. err.
{hline 13}{c +}{hline 64}
{space 2}satis_head {c |}{res}{col 18} .1001405{col 31} .1023982{col 47}-.0022576{col 63} .1560395
{txt}{space 4}thirties {c |}{res}{col 18}   .00605{col 31} .0060911{col 47}-.0000411{col 63} .0021466
{txt}{space 4}fourties {c |}{res}{col 18} .0162285{col 31} .0163058{col 47}-.0000774{col 63} .0049574
{txt}{space 5}fifties {c |}{res}{col 18} .0461833{col 31} .0462606{col 47}-.0000773{col 63} .0052515
{txt}{space 5}sixties {c |}{res}{col 18} .0688788{col 31} .0689589{col 47}-.0000801{col 63} .0055688
{txt}{space 3}seventies {c |}{res}{col 18}  .084055{col 31} .0840891{col 47}-.0000341{col 63}  .002171
{txt}income2qua~e {c |}{res}{col 18} .0282342{col 31} .0281968{col 47} .0000374{col 63} .0026946
{txt}income3qua~e {c |}{res}{col 18} .0469905{col 31} .0469364{col 47} .0000541{col 63} .0035896
{txt}income4qua~e {c |}{res}{col 18} .0510875{col 31} .0509931{col 47} .0000944{col 63} .0064607
{txt}incomenoan~r {c |}{res}{col 18} .0084894{col 31} .0085015{col 47}-.0000122{col 63} .0014718
{txt}{space 6}female {c |}{res}{col 18}-.0101624{col 31}-.0101964{col 47}  .000034{col 63} .0017334
{txt}{space 2}highschool {c |}{res}{col 18} .0239705{col 31} .0239526{col 47} .0000179{col 63} .0014096
{txt}{space 5}college {c |}{res}{col 18} .0670147{col 31} .0670183{col 47}-3.64e-06{col 63} .0003232
{txt}{space 2}noreligion {c |}{res}{col 18} .0393592{col 31} .0394337{col 47}-.0000745{col 63} .0048659
{txt}christiann~c {c |}{res}{col 18}  .029493{col 31} .0293499{col 47} .0001431{col 63} .0100001
{txt}{space 4}catholic {c |}{res}{col 18}  .035518{col 31} .0354713{col 47} .0000467{col 63} .0033745
{txt}fulltimewo~r {c |}{res}{col 18} .0159013{col 31} .0166043{col 47}-.0007029{col 63} .0487342
{txt}parttimewo~r {c |}{res}{col 18} .0417526{col 31} .0424428{col 47}-.0006902{col 63} .0477025
{txt}{space 2}unemployed {c |}{res}{col 18} .0121465{col 31}  .012916{col 47}-.0007694{col 63} .0539153
{txt}selfemployed {c |}{res}{col 18} .0129263{col 31} .0137822{col 47}-.0008559{col 63} .0577829
{txt}{space 5}outofLF {c |}{res}{col 18} .0284975{col 31}  .029226{col 47}-.0007284{col 63} .0506033
{txt}{space 2}goodhealth {c |}{res}{col 18}  .027603{col 31} .0274864{col 47} .0001167{col 63}  .007814
{txt}{space 7}white {c |}{res}{col 18} .0509299{col 31} .0506319{col 47}  .000298{col 63} .0205573
{txt}{space 7}black {c |}{res}{col 18} .0695418{col 31}  .069515{col 47} .0000269{col 63} .0017037
{txt}{space 6}latino {c |}{res}{col 18} .0966586{col 31} .0965484{col 47} .0001102{col 63} .0057105
{txt}{space 7}asian {c |}{res}{col 18} .0818914{col 31} .0816873{col 47} .0002041{col 63} .0115786
{txt}{space 1}whitecollar {c |}{res}{col 18}-.0120367{col 31}-.0120083{col 47}-.0000284{col 63} .0026177
{txt}{space 2}bluecollar {c |}{res}{col 18}-.0207766{col 31}-.0207131{col 47}-.0000635{col 63} .0037271
{txt}servicewor~r {c |}{res}{col 18}-.0113047{col 31}-.0112827{col 47} -.000022{col 63} .0015416
{txt}mv_incomen~r {c |}{res}{col 18} .0003907{col 31} .0005538{col 47}-.0001631{col 63} .0115498
{txt}mv_highsch~l {c |}{res}{col 18} .0251066{col 31} .0251337{col 47}-.0000271{col 63} .0017432
{txt}mv_norelig~n {c |}{res}{col 18} .0317811{col 31} .0317691{col 47}  .000012{col 63} .0007522
{txt}mv_parttim~r {c |}{res}{col 18} .0384054{col 31}   .03905{col 47}-.0006447{col 63} .0450082
{txt}mv_selfemp~d {c |}{res}{col 18}-.0659311{col 31} -.066599{col 47} .0006679{col 63} .0462552
{txt}mv_goodhea~h {c |}{res}{col 18}-.1836259{col 31}-.1839394{col 47} .0003136{col 63} .0272073
{txt}{space 4}mv_white {c |}{res}{col 18} .1294109{col 31} .1285453{col 47} .0008656{col 63} .0594807
{txt}mv_whiteco~r {c |}{res}{col 18}-.0698677{col 31}-.0705549{col 47} .0006872{col 63} .0473505
{txt}{space 5}country {c |}
{space 10}2  {c |}{res}{col 18} .1215785{col 31} .1204074{col 47} .0011711{col 63}  .080449
{txt}{space 10}3  {c |}{res}{col 18}-.0699205{col 31}-.0698866{col 47} -.000034{col 63} .0025515
{txt}{space 10}5  {c |}{res}{col 18} .0633849{col 31}  .062125{col 47} .0012599{col 63} .0865177
{txt}{space 10}6  {c |}{res}{col 18} .1055568{col 31} .1044462{col 47} .0011106{col 63} .0765826
{txt}{space 10}8  {c |}{res}{col 18}-.1056123{col 31} -.105539{col 47}-.0000733{col 63} .0053813
{txt}{space 9}10  {c |}{res}{col 18}-.0664929{col 31} -.066631{col 47}  .000138{col 63} .0091514
{txt}{hline 13}{c BT}{hline 64}
{ralign 78:b = Consistent under H0 and Ha; obtained from {bf:ivreg2}.}
{ralign 78:B = Inconsistent under Ha, efficient under H0; obtained from {bf:ivreg2}.}

Test of H0: Difference in coefficients not systematic

{ralign 8:chi2({res:39})} = (b-B)'[(V_b-V_B)^(-1)](b-B)
{col 9} = {res:-0.06}

{p 0 9 0 50}Warning: chi2 < 0 ==> model
fitted on these data fails to meet the asymptotic
assumptions of the Hausman test;
see {helpb suest##|_new:suest} for a 
generalized test.{p_end}

{com}. local temp_hausman = r(p)
{txt}
{com}. 
. /* E.15. Col.4. 8 instruments + controls */
. eststo: ivreg2 democ ///
>         (satis_head = TE2 TE4 TH2 TH4 TH4_TE2 TH4_TE4 TH2_TE2 TH2_TE4 ) bad_econ_situation bad_health_situation $controls $mv_controls i.country, small robust          
{txt}Warning - collinearities detected
Vars dropped:{col 21}mv_thirties mv_fourties mv_fifties mv_sixties mv_seventies
{col 21}mv_income2quartile mv_income3quartile mv_income4quartile
{col 21}mv_female mv_college mv_christiannotcatholic mv_catholic
{col 21}mv_fulltimeworker mv_unemployed mv_outofLF mv_black
{col 21}mv_latino mv_asian mv_bluecollar mv_serviceworker 4.country
{col 21}7.country 9.country 11.country 12.country
{res}
{txt}IV (2SLS) estimation
{hline 20}

Estimates efficient for homoskedasticity only
Statistics robust to heteroskedasticity

{col 55}Number of obs = {res}   22537
{txt}{col 55}F( 45, 22491) = {res}   17.97
{txt}{col 55}Prob > F      = {res}  0.0000
{txt}Total (centered) SS     = {res} 1987.655944{txt}{col 55}Centered R2   = {res}  0.0395
{txt}Total (uncentered) SS   = {res}       20334{txt}{col 55}Uncentered R2 = {res}  0.9061
{txt}Residual SS             = {res} 1909.184624{txt}{col 55}Root MSE      = {res}   .2914

{txt}{hline 24}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 25}{c |}{col 37}    Robust
{col 1}                  democ{col 25}{c |} Coefficient{col 37}  std. err.{col 49}      t{col 57}   P>|t|{col 65}     [95% con{col 78}f. interval]
{hline 24}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 13}satis_head {c |}{col 25}{res}{space 2} .1001405{col 37}{space 2}  .260183{col 48}{space 1}    0.38{col 57}{space 3}0.700{col 65}{space 4}-.4098363{col 78}{space 3} .6101173
{txt}{space 5}bad_econ_situation {c |}{col 25}{res}{space 2}-.0004501{col 37}{space 2} .0042042{col 48}{space 1}   -0.11{col 57}{space 3}0.915{col 65}{space 4}-.0086906{col 78}{space 3} .0077903
{txt}{space 3}bad_health_situation {c |}{col 25}{res}{space 2}-.0004223{col 37}{space 2} .0044293{col 48}{space 1}   -0.10{col 57}{space 3}0.924{col 65}{space 4} -.009104{col 78}{space 3} .0082594
{txt}{space 15}thirties {c |}{col 25}{res}{space 2}   .00605{col 37}{space 2} .0086896{col 48}{space 1}    0.70{col 57}{space 3}0.486{col 65}{space 4}-.0109822{col 78}{space 3} .0230821
{txt}{space 15}fourties {c |}{col 25}{res}{space 2} .0162285{col 37}{space 2} .0112922{col 48}{space 1}    1.44{col 57}{space 3}0.151{col 65}{space 4} -.005905{col 78}{space 3}  .038362
{txt}{space 16}fifties {c |}{col 25}{res}{space 2} .0461833{col 37}{space 2} .0116089{col 48}{space 1}    3.98{col 57}{space 3}0.000{col 65}{space 4}  .023429{col 78}{space 3} .0689377
{txt}{space 16}sixties {c |}{col 25}{res}{space 2} .0688788{col 37}{space 2} .0119789{col 48}{space 1}    5.75{col 57}{space 3}0.000{col 65}{space 4} .0453994{col 78}{space 3} .0923582
{txt}{space 14}seventies {c |}{col 25}{res}{space 2}  .084055{col 37}{space 2} .0088119{col 48}{space 1}    9.54{col 57}{space 3}0.000{col 65}{space 4}  .066783{col 78}{space 3}  .101327
{txt}{space 8}income2quartile {c |}{col 25}{res}{space 2} .0282342{col 37}{space 2}  .007709{col 48}{space 1}    3.66{col 57}{space 3}0.000{col 65}{space 4} .0131239{col 78}{space 3} .0433444
{txt}{space 8}income3quartile {c |}{col 25}{res}{space 2} .0469905{col 37}{space 2} .0085892{col 48}{space 1}    5.47{col 57}{space 3}0.000{col 65}{space 4}  .030155{col 78}{space 3}  .063826
{txt}{space 8}income4quartile {c |}{col 25}{res}{space 2} .0510875{col 37}{space 2} .0123414{col 48}{space 1}    4.14{col 57}{space 3}0.000{col 65}{space 4} .0268976{col 78}{space 3} .0752775
{txt}{space 9}incomenoanswer {c |}{col 25}{res}{space 2} .0084894{col 37}{space 2} .0099171{col 48}{space 1}    0.86{col 57}{space 3}0.392{col 65}{space 4}-.0109488{col 78}{space 3} .0279275
{txt}{space 17}female {c |}{col 25}{res}{space 2}-.0101624{col 37}{space 2} .0048387{col 48}{space 1}   -2.10{col 57}{space 3}0.036{col 65}{space 4}-.0196465{col 78}{space 3}-.0006782
{txt}{space 13}highschool {c |}{col 25}{res}{space 2} .0239705{col 37}{space 2} .0076976{col 48}{space 1}    3.11{col 57}{space 3}0.002{col 65}{space 4} .0088827{col 78}{space 3} .0390584
{txt}{space 16}college {c |}{col 25}{res}{space 2} .0670147{col 37}{space 2} .0066825{col 48}{space 1}   10.03{col 57}{space 3}0.000{col 65}{space 4} .0539165{col 78}{space 3} .0801128
{txt}{space 13}noreligion {c |}{col 25}{res}{space 2} .0393592{col 37}{space 2} .0126094{col 48}{space 1}    3.12{col 57}{space 3}0.002{col 65}{space 4} .0146439{col 78}{space 3} .0640744
{txt}{space 3}christiannotcatholic {c |}{col 25}{res}{space 2}  .029493{col 37}{space 2} .0198784{col 48}{space 1}    1.48{col 57}{space 3}0.138{col 65}{space 4}-.0094699{col 78}{space 3}  .068456
{txt}{space 15}catholic {c |}{col 25}{res}{space 2}  .035518{col 37}{space 2} .0114668{col 48}{space 1}    3.10{col 57}{space 3}0.002{col 65}{space 4} .0130423{col 78}{space 3} .0579937
{txt}{space 9}fulltimeworker {c |}{col 25}{res}{space 2} .0159013{col 37}{space 2} .0820144{col 48}{space 1}    0.19{col 57}{space 3}0.846{col 65}{space 4}-.1448525{col 78}{space 3} .1766552
{txt}{space 9}parttimeworker {c |}{col 25}{res}{space 2} .0417526{col 37}{space 2} .0829101{col 48}{space 1}    0.50{col 57}{space 3}0.615{col 65}{space 4} -.120757{col 78}{space 3} .2042623
{txt}{space 13}unemployed {c |}{col 25}{res}{space 2} .0121465{col 37}{space 2}  .091932{col 48}{space 1}    0.13{col 57}{space 3}0.895{col 65}{space 4}-.1680466{col 78}{space 3} .1923397
{txt}{space 11}selfemployed {c |}{col 25}{res}{space 2} .0129263{col 37}{space 2} .1004512{col 48}{space 1}    0.13{col 57}{space 3}0.898{col 65}{space 4} -.183965{col 78}{space 3} .2098175
{txt}{space 16}outofLF {c |}{col 25}{res}{space 2} .0284975{col 37}{space 2} .0851737{col 48}{space 1}    0.33{col 57}{space 3}0.738{col 65}{space 4}-.1384489{col 78}{space 3}  .195444
{txt}{space 13}goodhealth {c |}{col 25}{res}{space 2}  .027603{col 37}{space 2} .0138276{col 48}{space 1}    2.00{col 57}{space 3}0.046{col 65}{space 4}    .0005{col 78}{space 3} .0547061
{txt}{space 18}white {c |}{col 25}{res}{space 2} .0509299{col 37}{space 2}  .068237{col 48}{space 1}    0.75{col 57}{space 3}0.455{col 65}{space 4}-.0828194{col 78}{space 3} .1846792
{txt}{space 18}black {c |}{col 25}{res}{space 2} .0695418{col 37}{space 2}  .064302{col 48}{space 1}    1.08{col 57}{space 3}0.279{col 65}{space 4}-.0564945{col 78}{space 3} .1955782
{txt}{space 17}latino {c |}{col 25}{res}{space 2} .0966586{col 37}{space 2} .0687163{col 48}{space 1}    1.41{col 57}{space 3}0.160{col 65}{space 4}-.0380301{col 78}{space 3} .2313473
{txt}{space 18}asian {c |}{col 25}{res}{space 2} .0818914{col 37}{space 2}  .070172{col 48}{space 1}    1.17{col 57}{space 3}0.243{col 65}{space 4}-.0556505{col 78}{space 3} .2194334
{txt}{space 12}whitecollar {c |}{col 25}{res}{space 2}-.0120367{col 37}{space 2} .0107363{col 48}{space 1}   -1.12{col 57}{space 3}0.262{col 65}{space 4}-.0330805{col 78}{space 3} .0090071
{txt}{space 13}bluecollar {c |}{col 25}{res}{space 2}-.0207766{col 37}{space 2} .0129291{col 48}{space 1}   -1.61{col 57}{space 3}0.108{col 65}{space 4}-.0461185{col 78}{space 3} .0045654
{txt}{space 10}serviceworker {c |}{col 25}{res}{space 2}-.0113047{col 37}{space 2} .0088366{col 48}{space 1}   -1.28{col 57}{space 3}0.201{col 65}{space 4}-.0286251{col 78}{space 3} .0060157
{txt}{space 12}mv_thirties {c |}{col 25}{res}{space 2}        0{col 37}{txt}  (omitted)
{space 12}mv_fourties {c |}{col 25}{res}{space 2}        0{col 37}{txt}  (omitted)
{space 13}mv_fifties {c |}{col 25}{res}{space 2}        0{col 37}{txt}  (omitted)
{space 13}mv_sixties {c |}{col 25}{res}{space 2}        0{col 37}{txt}  (omitted)
{space 11}mv_seventies {c |}{col 25}{res}{space 2}        0{col 37}{txt}  (omitted)
{space 5}mv_income2quartile {c |}{col 25}{res}{space 2}        0{col 37}{txt}  (omitted)
{space 5}mv_income3quartile {c |}{col 25}{res}{space 2}        0{col 37}{txt}  (omitted)
{space 5}mv_income4quartile {c |}{col 25}{res}{space 2}        0{col 37}{txt}  (omitted)
{space 6}mv_incomenoanswer {c |}{col 25}{res}{space 2} .0003907{col 37}{space 2} .0229189{col 48}{space 1}    0.02{col 57}{space 3}0.986{col 65}{space 4}-.0445319{col 78}{space 3} .0453133
{txt}{space 14}mv_female {c |}{col 25}{res}{space 2}        0{col 37}{txt}  (omitted)
{space 10}mv_highschool {c |}{col 25}{res}{space 2} .0251066{col 37}{space 2} .0175946{col 48}{space 1}    1.43{col 57}{space 3}0.154{col 65}{space 4}  -.00938{col 78}{space 3} .0595932
{txt}{space 13}mv_college {c |}{col 25}{res}{space 2}        0{col 37}{txt}  (omitted)
{space 10}mv_noreligion {c |}{col 25}{res}{space 2} .0317811{col 37}{space 2} .0266981{col 48}{space 1}    1.19{col 57}{space 3}0.234{col 65}{space 4} -.020549{col 78}{space 3} .0841112
{txt}mv_christiannotcatholic {c |}{col 25}{res}{space 2}        0{col 37}{txt}  (omitted)
{space 12}mv_catholic {c |}{col 25}{res}{space 2}        0{col 37}{txt}  (omitted)
{space 6}mv_fulltimeworker {c |}{col 25}{res}{space 2}        0{col 37}{txt}  (omitted)
{space 6}mv_parttimeworker {c |}{col 25}{res}{space 2} .0384054{col 37}{space 2} .0783525{col 48}{space 1}    0.49{col 57}{space 3}0.624{col 65}{space 4}-.1151709{col 78}{space 3} .1919816
{txt}{space 10}mv_unemployed {c |}{col 25}{res}{space 2}        0{col 37}{txt}  (omitted)
{space 8}mv_selfemployed {c |}{col 25}{res}{space 2}-.0659311{col 37}{space 2} .0804689{col 48}{space 1}   -0.82{col 57}{space 3}0.413{col 65}{space 4}-.2236557{col 78}{space 3} .0917935
{txt}{space 13}mv_outofLF {c |}{col 25}{res}{space 2}        0{col 37}{txt}  (omitted)
{space 10}mv_goodhealth {c |}{col 25}{res}{space 2}-.1836259{col 37}{space 2} .1435656{col 48}{space 1}   -1.28{col 57}{space 3}0.201{col 65}{space 4}-.4650244{col 78}{space 3} .0977726
{txt}{space 15}mv_white {c |}{col 25}{res}{space 2} .1294109{col 37}{space 2} .1157522{col 48}{space 1}    1.12{col 57}{space 3}0.264{col 65}{space 4}-.0974714{col 78}{space 3} .3562932
{txt}{space 15}mv_black {c |}{col 25}{res}{space 2}        0{col 37}{txt}  (omitted)
{space 14}mv_latino {c |}{col 25}{res}{space 2}        0{col 37}{txt}  (omitted)
{space 15}mv_asian {c |}{col 25}{res}{space 2}        0{col 37}{txt}  (omitted)
{space 9}mv_whitecollar {c |}{col 25}{res}{space 2}-.0698677{col 37}{space 2} .0817579{col 48}{space 1}   -0.85{col 57}{space 3}0.393{col 65}{space 4}-.2301188{col 78}{space 3} .0903834
{txt}{space 10}mv_bluecollar {c |}{col 25}{res}{space 2}        0{col 37}{txt}  (omitted)
{space 7}mv_serviceworker {c |}{col 25}{res}{space 2}        0{col 37}{txt}  (omitted)
{space 23} {c |}
{space 16}country {c |}
{space 15}Austria  {c |}{col 25}{res}{space 2} .1215785{col 37}{space 2} .1481886{col 48}{space 1}    0.82{col 57}{space 3}0.412{col 65}{space 4}-.1688814{col 78}{space 3} .4120384
{txt}{space 16}Brazil  {c |}{col 25}{res}{space 2}-.0699205{col 37}{space 2} .0218501{col 48}{space 1}   -3.20{col 57}{space 3}0.001{col 65}{space 4}-.1127482{col 78}{space 3}-.0270928
{txt}{space 16}France  {c |}{col 25}{res}{space 2}        0{col 37}{txt}  (omitted)
{space 15}Germany  {c |}{col 25}{res}{space 2} .0633849{col 37}{space 2} .1575513{col 48}{space 1}    0.40{col 57}{space 3}0.687{col 65}{space 4}-.2454266{col 78}{space 3} .3721964
{txt}{space 17}Italy  {c |}{col 25}{res}{space 2} .1055568{col 37}{space 2}  .144691{col 48}{space 1}    0.73{col 57}{space 3}0.466{col 65}{space 4}-.1780475{col 78}{space 3} .3891612
{txt}{space 11}New_Zealand  {c |}{col 25}{res}{space 2}        0{col 37}{txt}  (omitted)
{space 16}Poland  {c |}{col 25}{res}{space 2}-.1056123{col 37}{space 2} .0239495{col 48}{space 1}   -4.41{col 57}{space 3}0.000{col 65}{space 4}-.1525549{col 78}{space 3}-.0586697
{txt}{space 17}Spain  {c |}{col 25}{res}{space 2}        0{col 37}{txt}  (omitted)
{space 16}Sweden  {c |}{col 25}{res}{space 2}-.0664929{col 37}{space 2} .0255819{col 48}{space 1}   -2.60{col 57}{space 3}0.009{col 65}{space 4}-.1166353{col 78}{space 3}-.0163506
{txt}{space 20}UK  {c |}{col 25}{res}{space 2}        0{col 37}{txt}  (omitted)
{space 20}US  {c |}{col 25}{res}{space 2}        0{col 37}{txt}  (omitted)
{space 23} {c |}
{space 18}_cons {c |}{col 25}{res}{space 2} .6640082{col 37}{space 2} .0632776{col 48}{space 1}   10.49{col 57}{space 3}0.000{col 65}{space 4} .5399796{col 78}{space 3} .7880368
{txt}{hline 24}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{help ivreg2##idtest:Underidentification test} (Kleibergen-Paap rk LM statistic):{res}{col 71}  14.977
{txt}{col 52}Chi-sq({res}8{txt}) P-val =  {res}{col 73}0.0596
{txt}{hline 78}
{help ivreg2##widtest:Weak identification test} (Cragg-Donald Wald F statistic):{res}{col 71}   1.840
{txt}                         (Kleibergen-Paap rk Wald F statistic):{res}{col 71}   1.871
{txt}Stock-Yogo weak ID test critical values:{res}{txt}{col 42} 5% maximal IV relative bias{res}{col 73} 20.25
{txt}{col 42}10% maximal IV relative bias{res}{col 73} 11.39
{txt}{col 42}20% maximal IV relative bias{res}{col 73}  6.69
{txt}{col 42}30% maximal IV relative bias{res}{col 73}  4.99
{txt}{col 42}10% maximal IV size{res}{col 73} 33.84
{txt}{col 42}15% maximal IV size{res}{col 73} 18.54
{txt}{col 42}20% maximal IV size{res}{col 73} 13.24
{txt}{col 42}25% maximal IV size{res}{col 73} 10.50
{txt}Source: Stock-Yogo (2005).  Reproduced by permission.
NB: Critical values are for Cragg-Donald F statistic and i.i.d. errors.
{hline 78}
{help ivreg2##overidtests:Hansen J statistic} (overidentification test of all instruments):{res}{col 71}   7.438
{txt}{col 52}Chi-sq({res}7{txt}) P-val =  {res}{col 73}0.3847
{txt}{hline 78}
Instrumented:{col 23}satis_head
Included instruments:{col 23}bad_econ_situation bad_health_situation thirties fourties
{col 23}fifties sixties seventies income2quartile income3quartile
{col 23}income4quartile incomenoanswer female highschool college
{col 23}noreligion christiannotcatholic catholic fulltimeworker
{col 23}parttimeworker unemployed selfemployed outofLF goodhealth
{col 23}white black latino asian whitecollar bluecollar
{col 23}serviceworker mv_incomenoanswer mv_highschool
{col 23}mv_noreligion mv_parttimeworker mv_selfemployed
{col 23}mv_goodhealth mv_white mv_whitecollar 2.country 3.country
{col 23}5.country 6.country 8.country 10.country
Excluded instruments:{col 23}TE2 TE4 TH2 TH4 TH4_TE2 TH4_TE4 TH2_TE2 TH2_TE4
Dropped collinear:{col 23}mv_thirties mv_fourties mv_fifties mv_sixties
{col 23}mv_seventies mv_income2quartile mv_income3quartile
{col 23}mv_income4quartile mv_female mv_college
{col 23}mv_christiannotcatholic mv_catholic mv_fulltimeworker
{col 23}mv_unemployed mv_outofLF mv_black mv_latino mv_asian
{col 23}mv_bluecollar mv_serviceworker 4.country 7.country
{col 23}9.country 11.country 12.country
{hline 78}
({res}est5{txt} stored)

{com}. 
. estimates store mod4
{txt}
{com}. estadd scalar fstat = e(widstat)

{txt}added scalar:
              e(fstat) =  {res}1.8711478
{txt}
{com}. estadd local CFE "X", replace

{txt}added macro:
                e(CFE) : "{res:X}"

{com}. estadd local Controls "X", replace

{txt}added macro:
           e(Controls) : "{res:X}"

{com}. estadd local IV "8 IVs"

{txt}added macro:
                 e(IV) : "{res:8 IVs}"

{com}. estadd local nothing nothing2 = " "

{txt}added macro:
            e(nothing) : "{res:nothing2 = " "}"

{com}. estadd scalar hausman = `temp_hausman'

{txt}added scalar:
            e(hausman) =  {res}1
{txt}
{com}. local coef4 = _b[satis_head]
{txt}
{com}. local se4 = _se[satis_head]
{txt}
{com}. get_mean

{txt}added scalar:
                 e(av) =  {res}.902
{txt}
{com}. 
. * Z-test
. local z_value = abs(`coef4'-`coef2')/sqrt(`se4'^2+`se2'^2)
{txt}
{com}. di `z_value'
{res}.00677298
{txt}
{com}. estadd scalar wald = (1-normal(`z_value'))*2

{txt}added scalar:
               e(wald) =  {res}.99459598
{txt}
{com}. 
. local labgen: variable label democ
{txt}
{com}. 
. esttab mod1 mod2 mod3 mod4 using "3_output/2_OA/Tables/TableE15.tex", depvar keep(satis_head bad_econ_situation bad_health_situation ) ///
>         label replace ///
>         cells(b(star fmt(%15.3fc)) se(par fmt(%15.3fc))) interaction(" $\times$ ") ///
>         star(* 0.10 ** 0.05 *** 0.01) ///
>         stats(Controls CFE N av IV fstat hausman wald, layout(@ @ @ @ @ @ @ @) fmt(%15s %15s %15.0fc %9.3f %9.3f %9.3f %9.3f %9.3f %9.3f ) ///
>         labels("Individual controls" "Country FE" Observations "Outcome mean" "Instruments" "F-statistic" "Hausman test p-value" "Z-test p-value")) ///
>         collabels(none) mlabels(none) ///
>         mgroups("`labgen'" , pattern(1 0 0 0) ///
>         prefix(\multicolumn{c -(}@span{c )-}{c -(}c{c )-}{c -(}) suffix({c )-}) span erepeat(\cmidrule(lr){c -(}@span{c )-}))
{res}{txt}(output written to {browse  `"3_output/2_OA/Tables/TableE15.tex"'})

{com}. 
.                         
. 
. 
. **# Table E.16: Impact on satisfaction with democracy, additional regressors - 2SLS.
. * Hausman test for following regression
. qui ivreg2 satis_dem ///
>         (satis_head = TH1_TE1 TH1_TE2 TH1_TE3 TH1_TE4 TH2_TE1 TH2_TE2 TH2_TE3 TH2_TE4 TH3_TE1 TH3_TE2 TH3_TE3 TH3_TE4 TH4_TE1 TH4_TE2 TH4_TE3), small
{txt}
{com}. estimates store mod1b
{txt}
{com}. 
. eststo clear
{txt}
{com}. /* E.16. Col.1. Initial coefficient: 16 instruments */
. * to get the Cragg-Donald statistic: 
. eststo: ivreg2 satis_dem ///
>         (satis_head = TH1_TE1 TH1_TE2 TH1_TE3 TH1_TE4 TH2_TE1 TH2_TE2 TH2_TE3 TH2_TE4 TH3_TE1 TH3_TE2 TH3_TE3 TH3_TE4 TH4_TE1 TH4_TE2 TH4_TE3), small robust
{res}
{txt}IV (2SLS) estimation
{hline 20}

Estimates efficient for homoskedasticity only
Statistics robust to heteroskedasticity

{col 55}Number of obs = {res}   22541
{txt}{col 55}F(  1, 22539) = {res}   12.20
{txt}{col 55}Prob > F      = {res}  0.0005
{txt}Total (centered) SS     = {res} 1635.679721{txt}{col 55}Centered R2   = {res}  0.3997
{txt}Total (uncentered) SS   = {res} 7268.530045{txt}{col 55}Uncentered R2 = {res}  0.8649
{txt}Residual SS             = {res} 981.8279294{txt}{col 55}Root MSE      = {res}   .2087

{txt}{hline 13}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 14}{c |}{col 26}    Robust
{col 1}   satis_dem{col 14}{c |} Coefficient{col 26}  std. err.{col 38}      t{col 46}   P>|t|{col 54}     [95% con{col 67}f. interval]
{hline 13}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 2}satis_head {c |}{col 14}{res}{space 2}  .521565{col 26}{space 2}  .149351{col 37}{space 1}    3.49{col 46}{space 3}0.000{col 54}{space 4} .2288268{col 67}{space 3} .8143032
{txt}{space 7}_cons {c |}{col 14}{res}{space 2} .2609145{col 26}{space 2}  .068446{col 37}{space 1}    3.81{col 46}{space 3}0.000{col 54}{space 4} .1267555{col 67}{space 3} .3950734
{txt}{hline 13}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{help ivreg2##idtest:Underidentification test} (Kleibergen-Paap rk LM statistic):{res}{col 71}  19.860
{txt}{col 52}Chi-sq({res}15{txt}) P-val =  {res}{col 73}0.1774
{txt}{hline 78}
{help ivreg2##widtest:Weak identification test} (Cragg-Donald Wald F statistic):{res}{col 71}   1.318
{txt}                         (Kleibergen-Paap rk Wald F statistic):{res}{col 71}   1.328
{txt}Stock-Yogo weak ID test critical values:{res}{txt}{col 42} 5% maximal IV relative bias{res}{col 73} 21.23
{txt}{col 42}10% maximal IV relative bias{res}{col 73} 11.51
{txt}{col 42}20% maximal IV relative bias{res}{col 73}  6.42
{txt}{col 42}30% maximal IV relative bias{res}{col 73}  4.63
{txt}{col 42}10% maximal IV size{res}{col 73} 50.39
{txt}{col 42}15% maximal IV size{res}{col 73} 26.80
{txt}{col 42}20% maximal IV size{res}{col 73} 18.72
{txt}{col 42}25% maximal IV size{res}{col 73} 14.60
{txt}Source: Stock-Yogo (2005).  Reproduced by permission.
NB: Critical values are for Cragg-Donald F statistic and i.i.d. errors.
{hline 78}
{help ivreg2##overidtests:Hansen J statistic} (overidentification test of all instruments):{res}{col 71}   7.648
{txt}{col 52}Chi-sq({res}14{txt}) P-val =  {res}{col 73}0.9068
{txt}{hline 78}
Instrumented:{col 23}satis_head
Excluded instruments:{col 23}TH1_TE1 TH1_TE2 TH1_TE3 TH1_TE4 TH2_TE1 TH2_TE2 TH2_TE3
{col 23}TH2_TE4 TH3_TE1 TH3_TE2 TH3_TE3 TH3_TE4 TH4_TE1 TH4_TE2
{col 23}TH4_TE3
{hline 78}
({res}est1{txt} stored)

{com}. 
. estimates store mod1
{txt}
{com}. estadd scalar fstat = e(widstat)

{txt}added scalar:
              e(fstat) =  {res}1.3283149
{txt}
{com}. estadd local IV "16 IVs"

{txt}added macro:
                 e(IV) : "{res:16 IVs}"

{com}. estadd local nothing nothing2 = " "

{txt}added macro:
            e(nothing) : "{res:nothing2 = " "}"

{com}. get_mean

{txt}added scalar:
                 e(av) =  {res}.5
{txt}
{com}. local coef1 = _b[satis_head]
{txt}
{com}. local se1 = _se[satis_head]
{txt}
{com}.         
. * Hausman test for following regression
. qui ivreg2 satis_dem ///
>         (satis_head = TH1_TE1 TH1_TE2 TH1_TE3 TH1_TE4 TH2_TE1 TH2_TE2 TH2_TE3 TH2_TE4 TH3_TE1 TH3_TE2 TH3_TE3 TH3_TE4 TH4_TE1 TH4_TE2 TH4_TE3) $controls $mv_controls i.country, small
{txt}
{com}. estimates store mod2b
{txt}
{com}. 
. /* E.16. Col.2. Initial coefficient: 16 instruments + controls */
. * to get the Cragg-Donald statistic: 
. eststo: ivreg2 satis_dem ///
>         (satis_head = TH1_TE1 TH1_TE2 TH1_TE3 TH1_TE4 TH2_TE1 TH2_TE2 TH2_TE3 TH2_TE4 TH3_TE1 TH3_TE2 TH3_TE3 TH3_TE4 TH4_TE1 TH4_TE2 TH4_TE3) $controls $mv_controls i.country, small robust
{txt}Warning - collinearities detected
Vars dropped:{col 21}mv_thirties mv_fourties mv_fifties mv_sixties mv_seventies
{col 21}mv_income2quartile mv_income3quartile mv_income4quartile
{col 21}mv_female mv_college mv_christiannotcatholic mv_catholic
{col 21}mv_fulltimeworker mv_unemployed mv_outofLF mv_black
{col 21}mv_latino mv_asian mv_bluecollar mv_serviceworker 4.country
{col 21}7.country 9.country 11.country 12.country
{res}
{txt}IV (2SLS) estimation
{hline 20}

Estimates efficient for homoskedasticity only
Statistics robust to heteroskedasticity

{col 55}Number of obs = {res}   22541
{txt}{col 55}F( 43, 22497) = {res}  114.64
{txt}{col 55}Prob > F      = {res}  0.0000
{txt}Total (centered) SS     = {res} 1635.679721{txt}{col 55}Centered R2   = {res}  0.4331
{txt}Total (uncentered) SS   = {res} 7268.530045{txt}{col 55}Uncentered R2 = {res}  0.8724
{txt}Residual SS             = {res} 927.3110743{txt}{col 55}Root MSE      = {res}    .203

{txt}{hline 24}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 25}{c |}{col 37}    Robust
{col 1}              satis_dem{col 25}{c |} Coefficient{col 37}  std. err.{col 49}      t{col 57}   P>|t|{col 65}     [95% con{col 78}f. interval]
{hline 24}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 13}satis_head {c |}{col 25}{res}{space 2} .5283618{col 37}{space 2} .1425757{col 48}{space 1}    3.71{col 57}{space 3}0.000{col 65}{space 4} .2489036{col 78}{space 3}   .80782
{txt}{space 15}thirties {c |}{col 25}{res}{space 2} -.015411{col 37}{space 2} .0050154{col 48}{space 1}   -3.07{col 57}{space 3}0.002{col 65}{space 4}-.0252415{col 78}{space 3}-.0055806
{txt}{space 15}fourties {c |}{col 25}{res}{space 2} -.011639{col 37}{space 2} .0064623{col 48}{space 1}   -1.80{col 57}{space 3}0.072{col 65}{space 4}-.0243056{col 78}{space 3} .0010276
{txt}{space 16}fifties {c |}{col 25}{res}{space 2}-.0108162{col 37}{space 2} .0068628{col 48}{space 1}   -1.58{col 57}{space 3}0.115{col 65}{space 4}-.0242678{col 78}{space 3} .0026354
{txt}{space 16}sixties {c |}{col 25}{res}{space 2} .0033724{col 37}{space 2} .0070662{col 48}{space 1}    0.48{col 57}{space 3}0.633{col 65}{space 4}-.0104778{col 78}{space 3} .0172225
{txt}{space 14}seventies {c |}{col 25}{res}{space 2} .0202027{col 37}{space 2} .0062979{col 48}{space 1}    3.21{col 57}{space 3}0.001{col 65}{space 4} .0078585{col 78}{space 3}  .032547
{txt}{space 8}income2quartile {c |}{col 25}{res}{space 2} .0192263{col 37}{space 2} .0046975{col 48}{space 1}    4.09{col 57}{space 3}0.000{col 65}{space 4} .0100188{col 78}{space 3} .0284338
{txt}{space 8}income3quartile {c |}{col 25}{res}{space 2} .0257428{col 37}{space 2} .0052717{col 48}{space 1}    4.88{col 57}{space 3}0.000{col 65}{space 4} .0154099{col 78}{space 3} .0360757
{txt}{space 8}income4quartile {c |}{col 25}{res}{space 2}  .041517{col 37}{space 2}  .007253{col 48}{space 1}    5.72{col 57}{space 3}0.000{col 65}{space 4} .0273006{col 78}{space 3} .0557333
{txt}{space 9}incomenoanswer {c |}{col 25}{res}{space 2} .0079892{col 37}{space 2} .0067101{col 48}{space 1}    1.19{col 57}{space 3}0.234{col 65}{space 4} -.005163{col 78}{space 3} .0211415
{txt}{space 17}female {c |}{col 25}{res}{space 2}-.0091862{col 37}{space 2} .0031529{col 48}{space 1}   -2.91{col 57}{space 3}0.004{col 65}{space 4}-.0153661{col 78}{space 3}-.0030063
{txt}{space 13}highschool {c |}{col 25}{res}{space 2} .0054292{col 37}{space 2} .0047662{col 48}{space 1}    1.14{col 57}{space 3}0.255{col 65}{space 4} -.003913{col 78}{space 3} .0147714
{txt}{space 16}college {c |}{col 25}{res}{space 2} .0270696{col 37}{space 2} .0042705{col 48}{space 1}    6.34{col 57}{space 3}0.000{col 65}{space 4} .0186992{col 78}{space 3} .0354401
{txt}{space 13}noreligion {c |}{col 25}{res}{space 2}-.0118266{col 37}{space 2} .0076726{col 48}{space 1}   -1.54{col 57}{space 3}0.123{col 65}{space 4}-.0268655{col 78}{space 3} .0032123
{txt}{space 3}christiannotcatholic {c |}{col 25}{res}{space 2}  .012457{col 37}{space 2} .0115508{col 48}{space 1}    1.08{col 57}{space 3}0.281{col 65}{space 4}-.0101833{col 78}{space 3} .0350973
{txt}{space 15}catholic {c |}{col 25}{res}{space 2} .0164308{col 37}{space 2} .0069826{col 48}{space 1}    2.35{col 57}{space 3}0.019{col 65}{space 4} .0027445{col 78}{space 3} .0301172
{txt}{space 9}fulltimeworker {c |}{col 25}{res}{space 2}-.0331149{col 37}{space 2} .0450563{col 48}{space 1}   -0.73{col 57}{space 3}0.462{col 65}{space 4}-.1214284{col 78}{space 3} .0551986
{txt}{space 9}parttimeworker {c |}{col 25}{res}{space 2}-.0069356{col 37}{space 2} .0470736{col 48}{space 1}   -0.15{col 57}{space 3}0.883{col 65}{space 4}-.0992032{col 78}{space 3}  .085332
{txt}{space 13}unemployed {c |}{col 25}{res}{space 2}-.0438316{col 37}{space 2} .0504433{col 48}{space 1}   -0.87{col 57}{space 3}0.385{col 65}{space 4}-.1427039{col 78}{space 3} .0550408
{txt}{space 11}selfemployed {c |}{col 25}{res}{space 2}-.0211849{col 37}{space 2} .0608035{col 48}{space 1}   -0.35{col 57}{space 3}0.728{col 65}{space 4}-.1403641{col 78}{space 3} .0979942
{txt}{space 16}outofLF {c |}{col 25}{res}{space 2}-.0326967{col 37}{space 2} .0467518{col 48}{space 1}   -0.70{col 57}{space 3}0.484{col 65}{space 4}-.1243334{col 78}{space 3}   .05894
{txt}{space 13}goodhealth {c |}{col 25}{res}{space 2} .0251379{col 37}{space 2} .0077078{col 48}{space 1}    3.26{col 57}{space 3}0.001{col 65}{space 4} .0100301{col 78}{space 3} .0402458
{txt}{space 18}white {c |}{col 25}{res}{space 2}-.0107805{col 37}{space 2} .0402719{col 48}{space 1}   -0.27{col 57}{space 3}0.789{col 65}{space 4}-.0897162{col 78}{space 3} .0681553
{txt}{space 18}black {c |}{col 25}{res}{space 2} .0669688{col 37}{space 2} .0387218{col 48}{space 1}    1.73{col 57}{space 3}0.084{col 65}{space 4}-.0089286{col 78}{space 3} .1428662
{txt}{space 17}latino {c |}{col 25}{res}{space 2} .0547505{col 37}{space 2} .0408108{col 48}{space 1}    1.34{col 57}{space 3}0.180{col 65}{space 4}-.0252414{col 78}{space 3} .1347425
{txt}{space 18}asian {c |}{col 25}{res}{space 2} .0344473{col 37}{space 2} .0414733{col 48}{space 1}    0.83{col 57}{space 3}0.406{col 65}{space 4}-.0468433{col 78}{space 3} .1157378
{txt}{space 12}whitecollar {c |}{col 25}{res}{space 2}  .001631{col 37}{space 2} .0076523{col 48}{space 1}    0.21{col 57}{space 3}0.831{col 65}{space 4}-.0133682{col 78}{space 3} .0166301
{txt}{space 13}bluecollar {c |}{col 25}{res}{space 2}-.0101123{col 37}{space 2} .0081638{col 48}{space 1}   -1.24{col 57}{space 3}0.215{col 65}{space 4} -.026114{col 78}{space 3} .0058893
{txt}{space 10}serviceworker {c |}{col 25}{res}{space 2} -.010438{col 37}{space 2} .0059797{col 48}{space 1}   -1.75{col 57}{space 3}0.081{col 65}{space 4}-.0221586{col 78}{space 3} .0012825
{txt}{space 12}mv_thirties {c |}{col 25}{res}{space 2}        0{col 37}{txt}  (omitted)
{space 12}mv_fourties {c |}{col 25}{res}{space 2}        0{col 37}{txt}  (omitted)
{space 13}mv_fifties {c |}{col 25}{res}{space 2}        0{col 37}{txt}  (omitted)
{space 13}mv_sixties {c |}{col 25}{res}{space 2}        0{col 37}{txt}  (omitted)
{space 11}mv_seventies {c |}{col 25}{res}{space 2}        0{col 37}{txt}  (omitted)
{space 5}mv_income2quartile {c |}{col 25}{res}{space 2}        0{col 37}{txt}  (omitted)
{space 5}mv_income3quartile {c |}{col 25}{res}{space 2}        0{col 37}{txt}  (omitted)
{space 5}mv_income4quartile {c |}{col 25}{res}{space 2}        0{col 37}{txt}  (omitted)
{space 6}mv_incomenoanswer {c |}{col 25}{res}{space 2} .0291219{col 37}{space 2} .0135794{col 48}{space 1}    2.14{col 57}{space 3}0.032{col 65}{space 4} .0025053{col 78}{space 3} .0557385
{txt}{space 14}mv_female {c |}{col 25}{res}{space 2}        0{col 37}{txt}  (omitted)
{space 10}mv_highschool {c |}{col 25}{res}{space 2} .0272225{col 37}{space 2} .0106467{col 48}{space 1}    2.56{col 57}{space 3}0.011{col 65}{space 4} .0063543{col 78}{space 3} .0480908
{txt}{space 13}mv_college {c |}{col 25}{res}{space 2}        0{col 37}{txt}  (omitted)
{space 10}mv_noreligion {c |}{col 25}{res}{space 2} .0005398{col 37}{space 2} .0233189{col 48}{space 1}    0.02{col 57}{space 3}0.982{col 65}{space 4}-.0451669{col 78}{space 3} .0462465
{txt}mv_christiannotcatholic {c |}{col 25}{res}{space 2}        0{col 37}{txt}  (omitted)
{space 12}mv_catholic {c |}{col 25}{res}{space 2}        0{col 37}{txt}  (omitted)
{space 6}mv_fulltimeworker {c |}{col 25}{res}{space 2}        0{col 37}{txt}  (omitted)
{space 6}mv_parttimeworker {c |}{col 25}{res}{space 2}-.0158404{col 37}{space 2} .0436233{col 48}{space 1}   -0.36{col 57}{space 3}0.717{col 65}{space 4}-.1013451{col 78}{space 3} .0696644
{txt}{space 10}mv_unemployed {c |}{col 25}{res}{space 2}        0{col 37}{txt}  (omitted)
{space 8}mv_selfemployed {c |}{col 25}{res}{space 2} .0308075{col 37}{space 2} .0450466{col 48}{space 1}    0.68{col 57}{space 3}0.494{col 65}{space 4}-.0574871{col 78}{space 3}  .119102
{txt}{space 13}mv_outofLF {c |}{col 25}{res}{space 2}        0{col 37}{txt}  (omitted)
{space 10}mv_goodhealth {c |}{col 25}{res}{space 2} .1184423{col 37}{space 2} .0760623{col 48}{space 1}    1.56{col 57}{space 3}0.119{col 65}{space 4}-.0306451{col 78}{space 3} .2675297
{txt}{space 15}mv_white {c |}{col 25}{res}{space 2} .0304671{col 37}{space 2} .0648308{col 48}{space 1}    0.47{col 57}{space 3}0.638{col 65}{space 4}-.0966058{col 78}{space 3} .1575399
{txt}{space 15}mv_black {c |}{col 25}{res}{space 2}        0{col 37}{txt}  (omitted)
{space 14}mv_latino {c |}{col 25}{res}{space 2}        0{col 37}{txt}  (omitted)
{space 15}mv_asian {c |}{col 25}{res}{space 2}        0{col 37}{txt}  (omitted)
{space 9}mv_whitecollar {c |}{col 25}{res}{space 2}-.0238005{col 37}{space 2} .0455043{col 48}{space 1}   -0.52{col 57}{space 3}0.601{col 65}{space 4} -.112992{col 78}{space 3}  .065391
{txt}{space 10}mv_bluecollar {c |}{col 25}{res}{space 2}        0{col 37}{txt}  (omitted)
{space 7}mv_serviceworker {c |}{col 25}{res}{space 2}        0{col 37}{txt}  (omitted)
{space 23} {c |}
{space 16}country {c |}
{space 15}Austria  {c |}{col 25}{res}{space 2} .0413313{col 37}{space 2} .0828504{col 48}{space 1}    0.50{col 57}{space 3}0.618{col 65}{space 4}-.1210612{col 78}{space 3} .2037239
{txt}{space 16}Brazil  {c |}{col 25}{res}{space 2}-.0732144{col 37}{space 2} .0149202{col 48}{space 1}   -4.91{col 57}{space 3}0.000{col 65}{space 4} -.102459{col 78}{space 3}-.0439698
{txt}{space 16}France  {c |}{col 25}{res}{space 2}        0{col 37}{txt}  (omitted)
{space 15}Germany  {c |}{col 25}{res}{space 2} .0201346{col 37}{space 2} .0877115{col 48}{space 1}    0.23{col 57}{space 3}0.818{col 65}{space 4} -.151786{col 78}{space 3} .1920553
{txt}{space 17}Italy  {c |}{col 25}{res}{space 2}-.0710875{col 37}{space 2} .0827082{col 48}{space 1}   -0.86{col 57}{space 3}0.390{col 65}{space 4}-.2332012{col 78}{space 3} .0910263
{txt}{space 11}New_Zealand  {c |}{col 25}{res}{space 2}        0{col 37}{txt}  (omitted)
{space 16}Poland  {c |}{col 25}{res}{space 2}-.0948499{col 37}{space 2} .0145928{col 48}{space 1}   -6.50{col 57}{space 3}0.000{col 65}{space 4}-.1234528{col 78}{space 3} -.066247
{txt}{space 17}Spain  {c |}{col 25}{res}{space 2}        0{col 37}{txt}  (omitted)
{space 16}Sweden  {c |}{col 25}{res}{space 2} .0625265{col 37}{space 2} .0161997{col 48}{space 1}    3.86{col 57}{space 3}0.000{col 65}{space 4}  .030774{col 78}{space 3} .0942791
{txt}{space 20}UK  {c |}{col 25}{res}{space 2}        0{col 37}{txt}  (omitted)
{space 20}US  {c |}{col 25}{res}{space 2}        0{col 37}{txt}  (omitted)
{space 23} {c |}
{space 18}_cons {c |}{col 25}{res}{space 2} .2057792{col 37}{space 2} .0387972{col 48}{space 1}    5.30{col 57}{space 3}0.000{col 65}{space 4} .1297341{col 78}{space 3} .2818243
{txt}{hline 24}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{help ivreg2##idtest:Underidentification test} (Kleibergen-Paap rk LM statistic):{res}{col 71}  23.634
{txt}{col 52}Chi-sq({res}15{txt}) P-val =  {res}{col 73}0.0716
{txt}{hline 78}
{help ivreg2##widtest:Weak identification test} (Cragg-Donald Wald F statistic):{res}{col 71}   1.548
{txt}                         (Kleibergen-Paap rk Wald F statistic):{res}{col 71}   1.578
{txt}Stock-Yogo weak ID test critical values:{res}{txt}{col 42} 5% maximal IV relative bias{res}{col 73} 21.23
{txt}{col 42}10% maximal IV relative bias{res}{col 73} 11.51
{txt}{col 42}20% maximal IV relative bias{res}{col 73}  6.42
{txt}{col 42}30% maximal IV relative bias{res}{col 73}  4.63
{txt}{col 42}10% maximal IV size{res}{col 73} 50.39
{txt}{col 42}15% maximal IV size{res}{col 73} 26.80
{txt}{col 42}20% maximal IV size{res}{col 73} 18.72
{txt}{col 42}25% maximal IV size{res}{col 73} 14.60
{txt}Source: Stock-Yogo (2005).  Reproduced by permission.
NB: Critical values are for Cragg-Donald F statistic and i.i.d. errors.
{hline 78}
{help ivreg2##overidtests:Hansen J statistic} (overidentification test of all instruments):{res}{col 71}   7.601
{txt}{col 52}Chi-sq({res}14{txt}) P-val =  {res}{col 73}0.9091
{txt}{hline 78}
Instrumented:{col 23}satis_head
Included instruments:{col 23}thirties fourties fifties sixties seventies
{col 23}income2quartile income3quartile income4quartile
{col 23}incomenoanswer female highschool college noreligion
{col 23}christiannotcatholic catholic fulltimeworker
{col 23}parttimeworker unemployed selfemployed outofLF goodhealth
{col 23}white black latino asian whitecollar bluecollar
{col 23}serviceworker mv_incomenoanswer mv_highschool
{col 23}mv_noreligion mv_parttimeworker mv_selfemployed
{col 23}mv_goodhealth mv_white mv_whitecollar 2.country 3.country
{col 23}5.country 6.country 8.country 10.country
Excluded instruments:{col 23}TH1_TE1 TH1_TE2 TH1_TE3 TH1_TE4 TH2_TE1 TH2_TE2 TH2_TE3
{col 23}TH2_TE4 TH3_TE1 TH3_TE2 TH3_TE3 TH3_TE4 TH4_TE1 TH4_TE2
{col 23}TH4_TE3
Dropped collinear:{col 23}mv_thirties mv_fourties mv_fifties mv_sixties
{col 23}mv_seventies mv_income2quartile mv_income3quartile
{col 23}mv_income4quartile mv_female mv_college
{col 23}mv_christiannotcatholic mv_catholic mv_fulltimeworker
{col 23}mv_unemployed mv_outofLF mv_black mv_latino mv_asian
{col 23}mv_bluecollar mv_serviceworker 4.country 7.country
{col 23}9.country 11.country 12.country
{hline 78}
({res}est2{txt} stored)

{com}. 
. estimates store mod2
{txt}
{com}. estadd scalar fstat = e(widstat)

{txt}added scalar:
              e(fstat) =  {res}1.5781581
{txt}
{com}. estadd local CFE "X", replace

{txt}added macro:
                e(CFE) : "{res:X}"

{com}. estadd local Controls "X", replace

{txt}added macro:
           e(Controls) : "{res:X}"

{com}. estadd local IV "16 IVs"

{txt}added macro:
                 e(IV) : "{res:16 IVs}"

{com}. estadd local nothing nothing2 = " "

{txt}added macro:
            e(nothing) : "{res:nothing2 = " "}"

{com}. get_mean

{txt}added scalar:
                 e(av) =  {res}.5
{txt}
{com}. local coef2 = _b[satis_head]
{txt}
{com}. local se2 = _se[satis_head]
{txt}
{com}. 
. * Hausman test for following regression
. qui ivreg2 satis_dem ///
>         (satis_head serious_h_csqc serious_e_csqc = TH1_TE1 TH1_TE2 TH1_TE3 TH1_TE4 TH2_TE1 TH2_TE2 TH2_TE3 TH2_TE4 TH3_TE1 TH3_TE2 TH3_TE3 TH3_TE4 TH4_TE1 TH4_TE2 TH4_TE3), small 
{txt}
{com}. estimates store mod3b
{txt}
{com}. 
. hausman mod3b mod1b

{txt}{col 18}{hline 4} Coefficients {hline 4}
{col 14}{c |}{col 21}(b){col 34}(B){col 49}(b-B){col 59}sqrt(diag(V_b-V_B))
{col 14}{c |}{col 17}   mod3b    {col 30}   mod1b    {col 46}Difference{col 63}Std. err.
{hline 13}{c +}{hline 64}
{space 2}satis_head {c |}{res}{col 18} .4432341{col 31}  .521565{col 47}-.0783309{col 63} .1305459
{txt}{hline 13}{c BT}{hline 64}
{ralign 78:b = Consistent under H0 and Ha; obtained from {bf:ivreg2}.}
{ralign 78:B = Inconsistent under Ha, efficient under H0; obtained from {bf:ivreg2}.}

Test of H0: Difference in coefficients not systematic

{ralign 11:chi2({res:1})} = (b-B)'[(V_b-V_B)^(-1)](b-B)
{ralign 11:} = {res:{ralign 6:0.36}}
{ralign 11:Prob > chi2} = {res:{ralign 6:0.5485}}

{com}. local temp_hausman = r(p)
{txt}
{com}. 
. /* E.16. Col.3. 16 instruments + additional endogenous variables */
. * to get the Cragg-Donald statistic: 
. eststo: ivreg2 satis_dem ///
>         (satis_head serious_h_csqc serious_e_csqc = TH1_TE1 TH1_TE2 TH1_TE3 TH1_TE4 TH2_TE1 TH2_TE2 TH2_TE3 TH2_TE4 TH3_TE1 TH3_TE2 TH3_TE3 TH3_TE4 TH4_TE1 TH4_TE2 TH4_TE3), small robust
{res}
{txt}IV (2SLS) estimation
{hline 20}

Estimates efficient for homoskedasticity only
Statistics robust to heteroskedasticity

{col 55}Number of obs = {res}   22537
{txt}{col 55}F(  3, 22533) = {res}    4.13
{txt}{col 55}Prob > F      = {res}  0.0062
{txt}Total (centered) SS     = {res} 1635.419721{txt}{col 55}Centered R2   = {res}  0.3886
{txt}Total (uncentered) SS   = {res} 7267.270045{txt}{col 55}Uncentered R2 = {res}  0.8624
{txt}Residual SS             = {res} 999.9347149{txt}{col 55}Root MSE      = {res}   .2107

{txt}{hline 15}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 16}{c |}{col 28}    Robust
{col 1}     satis_dem{col 16}{c |} Coefficient{col 28}  std. err.{col 40}      t{col 48}   P>|t|{col 56}     [95% con{col 69}f. interval]
{hline 15}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 4}satis_head {c |}{col 16}{res}{space 2} .4432341{col 28}{space 2}  .198915{col 39}{space 1}    2.23{col 48}{space 3}0.026{col 56}{space 4} .0533468{col 69}{space 3} .8331213
{txt}serious_h_csqc {c |}{col 16}{res}{space 2}-.0259563{col 28}{space 2} .0437993{col 39}{space 1}   -0.59{col 48}{space 3}0.553{col 56}{space 4} -.111806{col 69}{space 3} .0598934
{txt}serious_e_csqc {c |}{col 16}{res}{space 2}-.0079334{col 28}{space 2} .0368014{col 39}{space 1}   -0.22{col 48}{space 3}0.829{col 56}{space 4}-.0800666{col 69}{space 3} .0641999
{txt}{space 9}_cons {c |}{col 16}{res}{space 2} .3060846{col 28}{space 2} .1025041{col 39}{space 1}    2.99{col 48}{space 3}0.003{col 56}{space 4} .1051695{col 69}{space 3} .5069996
{txt}{hline 15}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{help ivreg2##idtest:Underidentification test} (Kleibergen-Paap rk LM statistic):{res}{col 71}  12.093
{txt}{col 52}Chi-sq({res}13{txt}) P-val =  {res}{col 73}0.5200
{txt}{hline 78}
{help ivreg2##widtest:Weak identification test} (Cragg-Donald Wald F statistic):{res}{col 71}   0.805
{txt}                         (Kleibergen-Paap rk Wald F statistic):{res}{col 71}   0.807
{txt}Stock-Yogo weak ID test critical values:{res}{txt}{col 42} 5% maximal IV relative bias{res}{col 73} 18.73
{txt}{col 42}10% maximal IV relative bias{res}{col 73} 10.33
{txt}{col 42}20% maximal IV relative bias{res}{col 73}  5.94
{txt}{col 42}30% maximal IV relative bias{res}{col 73}  4.37
{txt}Source: Stock-Yogo (2005).  Reproduced by permission.
NB: Critical values are for Cragg-Donald F statistic and i.i.d. errors.
{hline 78}
{help ivreg2##overidtests:Hansen J statistic} (overidentification test of all instruments):{res}{col 71}   7.050
{txt}{col 52}Chi-sq({res}12{txt}) P-val =  {res}{col 73}0.8543
{txt}{hline 78}
Instrumented:{col 23}satis_head serious_h_csqc serious_e_csqc
Excluded instruments:{col 23}TH1_TE1 TH1_TE2 TH1_TE3 TH1_TE4 TH2_TE1 TH2_TE2 TH2_TE3
{col 23}TH2_TE4 TH3_TE1 TH3_TE2 TH3_TE3 TH3_TE4 TH4_TE1 TH4_TE2
{col 23}TH4_TE3
{hline 78}
({res}est3{txt} stored)

{com}. 
. estimates store mod3
{txt}
{com}. estadd scalar fstat = e(widstat)

{txt}added scalar:
              e(fstat) =  {res}.80666662
{txt}
{com}. estadd local IV "16 IVs"

{txt}added macro:
                 e(IV) : "{res:16 IVs}"

{com}. estadd local nothing nothing2 = " "

{txt}added macro:
            e(nothing) : "{res:nothing2 = " "}"

{com}. get_mean

{txt}added scalar:
                 e(av) =  {res}.5
{txt}
{com}. estadd scalar hausman = `temp_hausman'

{txt}added scalar:
            e(hausman) =  {res}.54848911
{txt}
{com}. 
. local coef3 = _b[satis_head]
{txt}
{com}. local se3 = _se[satis_head]
{txt}
{com}. 
. * Z-test 
. local z_value = abs(`coef3'-`coef1')/sqrt(`se3'^2+`se1'^2)
{txt}
{com}. di (1-normal(`z_value'))*2
{res}.75283196
{txt}
{com}. estadd scalar wald = (1-normal(`z_value'))*2

{txt}added scalar:
               e(wald) =  {res}.75283196
{txt}
{com}. 
. * Hausman test for following regression
. qui ivreg2 satis_dem ///
>         (satis_head serious_h_csqc serious_e_csqc = TH1_TE1 TH1_TE2 TH1_TE3 TH1_TE4 TH2_TE1 TH2_TE2 TH2_TE3 TH2_TE4 TH3_TE1 TH3_TE2 TH3_TE3 TH3_TE4 TH4_TE1 TH4_TE2 TH4_TE3) $controls $mv_controls i.country, small 
{txt}
{com}. estimates store mod4b
{txt}
{com}. hausman mod4b mod2b

{txt}{col 18}{hline 4} Coefficients {hline 4}
{col 14}{c |}{col 21}(b){col 34}(B){col 49}(b-B){col 59}sqrt(diag(V_b-V_B))
{col 14}{c |}{col 17}   mod4b    {col 30}   mod2b    {col 46}Difference{col 63}Std. err.
{hline 13}{c +}{hline 64}
{space 2}satis_head {c |}{res}{col 18} .4489999{col 31} .5283618{col 47}-.0793619{col 63} .1177053
{txt}{space 4}thirties {c |}{res}{col 18}-.0160709{col 31} -.015411{col 47}-.0006599{col 63} .0010114
{txt}{space 4}fourties {c |}{res}{col 18}-.0135008{col 31} -.011639{col 47}-.0018618{col 63} .0023742
{txt}{space 5}fifties {c |}{res}{col 18}-.0124242{col 31}-.0108162{col 47} -.001608{col 63}  .002187
{txt}{space 5}sixties {c |}{res}{col 18} .0012966{col 31} .0033724{col 47}-.0020758{col 63} .0027133
{txt}{space 3}seventies {c |}{res}{col 18} .0197261{col 31} .0202027{col 47}-.0004766{col 63} .0019118
{txt}income2qua~e {c |}{res}{col 18} .0200783{col 31} .0192263{col 47}  .000852{col 63} .0013818
{txt}income3qua~e {c |}{res}{col 18} .0265048{col 31} .0257428{col 47}  .000762{col 63} .0015921
{txt}income4qua~e {c |}{res}{col 18}   .04384{col 31}  .041517{col 47}  .002323{col 63} .0038631
{txt}incomenoan~r {c |}{res}{col 18} .0070993{col 31} .0079892{col 47}-.0008899{col 63} .0015871
{txt}{space 6}female {c |}{res}{col 18} -.007511{col 31}-.0091862{col 47} .0016752{col 63} .0022225
{txt}{space 2}highschool {c |}{res}{col 18}  .005614{col 31} .0054292{col 47} .0001848{col 63} .0010711
{txt}{space 5}college {c |}{res}{col 18}  .026412{col 31} .0270696{col 47}-.0006576{col 63} .0010943
{txt}{space 2}noreligion {c |}{res}{col 18}-.0156185{col 31}-.0118266{col 47}-.0037919{col 63} .0049928
{txt}christiann~c {c |}{res}{col 18} .0162013{col 31}  .012457{col 47} .0037443{col 63}  .006185
{txt}{space 4}catholic {c |}{res}{col 18} .0177092{col 31} .0164308{col 47} .0012784{col 63} .0028374
{txt}fulltimewo~r {c |}{res}{col 18}-.0573909{col 31}-.0331149{col 47} -.024276{col 63} .0361065
{txt}parttimewo~r {c |}{res}{col 18}-.0302978{col 31}-.0069356{col 47}-.0233622{col 63} .0344351
{txt}{space 2}unemployed {c |}{res}{col 18}-.0712116{col 31}-.0438316{col 47}  -.02738{col 63} .0397343
{txt}selfemployed {c |}{res}{col 18}-.0516973{col 31}-.0211849{col 47}-.0305124{col 63} .0444776
{txt}{space 5}outofLF {c |}{res}{col 18}-.0581981{col 31}-.0326967{col 47}-.0255015{col 63} .0374364
{txt}{space 2}goodhealth {c |}{res}{col 18} .0280551{col 31} .0251379{col 47} .0029171{col 63} .0052708
{txt}{space 7}white {c |}{res}{col 18}  .000572{col 31}-.0107805{col 47} .0113525{col 63} .0164476
{txt}{space 7}black {c |}{res}{col 18} .0753045{col 31} .0669688{col 47} .0083357{col 63} .0110079
{txt}{space 6}latino {c |}{res}{col 18} .0621193{col 31} .0547505{col 47} .0073688{col 63} .0099001
{txt}{space 7}asian {c |}{res}{col 18} .0401871{col 31} .0344473{col 47} .0057398{col 63} .0086859
{txt}{space 1}whitecollar {c |}{res}{col 18} .0010607{col 31}  .001631{col 47}-.0005703{col 63} .0011268
{txt}{space 2}bluecollar {c |}{res}{col 18}-.0117794{col 31}-.0101123{col 47}-.0016671{col 63} .0023528
{txt}servicewor~r {c |}{res}{col 18} -.011676{col 31} -.010438{col 47} -.001238{col 63} .0018538
{txt}mv_incomen~r {c |}{res}{col 18} .0291764{col 31} .0291219{col 47} .0000545{col 63} .0014771
{txt}mv_highsch~l {c |}{res}{col 18} .0261312{col 31} .0272225{col 47}-.0010914{col 63} .0016581
{txt}mv_norelig~n {c |}{res}{col 18} .0013158{col 31} .0005398{col 47}  .000776{col 63} .0025336
{txt}mv_parttim~r {c |}{res}{col 18}-.0384175{col 31}-.0158404{col 47}-.0225772{col 63} .0330795
{txt}mv_selfemp~d {c |}{res}{col 18} .0455695{col 31} .0308075{col 47}  .014762{col 63} .0214137
{txt}mv_goodhea~h {c |}{res}{col 18} .1366673{col 31} .1184423{col 47} .0182251{col 63} .0284105
{txt}{space 4}mv_white {c |}{res}{col 18} .0581903{col 31} .0304671{col 47} .0277232{col 63} .0429305
{txt}mv_whiteco~r {c |}{res}{col 18}-.0087606{col 31}-.0238005{col 47} .0150399{col 63} .0233811
{txt}{space 5}country {c |}
{space 10}2  {c |}{res}{col 18} .0677091{col 31} .0413313{col 47} .0263777{col 63} .0430707
{txt}{space 10}3  {c |}{res}{col 18}-.0663667{col 31}-.0732144{col 47} .0068477{col 63} .0086146
{txt}{space 10}5  {c |}{res}{col 18} .0504373{col 31} .0201346{col 47} .0303026{col 63} .0479123
{txt}{space 10}6  {c |}{res}{col 18}-.0443176{col 31}-.0710875{col 47} .0267699{col 63} .0463342
{txt}{space 10}8  {c |}{res}{col 18}-.1069439{col 31}-.0948499{col 47} -.012094{col 63} .0172494
{txt}{space 9}10  {c |}{res}{col 18} .0613189{col 31} .0625265{col 47}-.0012077{col 63} .0051419
{txt}{hline 13}{c BT}{hline 64}
{ralign 78:b = Consistent under H0 and Ha; obtained from {bf:ivreg2}.}
{ralign 78:B = Inconsistent under Ha, efficient under H0; obtained from {bf:ivreg2}.}

Test of H0: Difference in coefficients not systematic

{ralign 11:chi2({res:43})} = (b-B)'[(V_b-V_B)^(-1)](b-B)
{ralign 11:} = {res:{ralign 6:1.21}}
{ralign 11:Prob > chi2} = {res:{ralign 6:1.0000}}

{com}. local temp_hausman = r(p)
{txt}
{com}.  
. /* E.16. Col.4. 16 instruments + additional endogenous variables + controls */
. * to get the Cragg-Donald statistic: 
. eststo: ivreg2 satis_dem ///
>         (satis_head serious_h_csqc serious_e_csqc = TH1_TE1 TH1_TE2 TH1_TE3 TH1_TE4 TH2_TE1 TH2_TE2 TH2_TE3 TH2_TE4 TH3_TE1 TH3_TE2 TH3_TE3 TH3_TE4 TH4_TE1 TH4_TE2 TH4_TE3), small robust
{res}
{txt}IV (2SLS) estimation
{hline 20}

Estimates efficient for homoskedasticity only
Statistics robust to heteroskedasticity

{col 55}Number of obs = {res}   22537
{txt}{col 55}F(  3, 22533) = {res}    4.13
{txt}{col 55}Prob > F      = {res}  0.0062
{txt}Total (centered) SS     = {res} 1635.419721{txt}{col 55}Centered R2   = {res}  0.3886
{txt}Total (uncentered) SS   = {res} 7267.270045{txt}{col 55}Uncentered R2 = {res}  0.8624
{txt}Residual SS             = {res} 999.9347149{txt}{col 55}Root MSE      = {res}   .2107

{txt}{hline 15}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 16}{c |}{col 28}    Robust
{col 1}     satis_dem{col 16}{c |} Coefficient{col 28}  std. err.{col 40}      t{col 48}   P>|t|{col 56}     [95% con{col 69}f. interval]
{hline 15}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 4}satis_head {c |}{col 16}{res}{space 2} .4432341{col 28}{space 2}  .198915{col 39}{space 1}    2.23{col 48}{space 3}0.026{col 56}{space 4} .0533468{col 69}{space 3} .8331213
{txt}serious_h_csqc {c |}{col 16}{res}{space 2}-.0259563{col 28}{space 2} .0437993{col 39}{space 1}   -0.59{col 48}{space 3}0.553{col 56}{space 4} -.111806{col 69}{space 3} .0598934
{txt}serious_e_csqc {c |}{col 16}{res}{space 2}-.0079334{col 28}{space 2} .0368014{col 39}{space 1}   -0.22{col 48}{space 3}0.829{col 56}{space 4}-.0800666{col 69}{space 3} .0641999
{txt}{space 9}_cons {c |}{col 16}{res}{space 2} .3060846{col 28}{space 2} .1025041{col 39}{space 1}    2.99{col 48}{space 3}0.003{col 56}{space 4} .1051695{col 69}{space 3} .5069996
{txt}{hline 15}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{help ivreg2##idtest:Underidentification test} (Kleibergen-Paap rk LM statistic):{res}{col 71}  12.093
{txt}{col 52}Chi-sq({res}13{txt}) P-val =  {res}{col 73}0.5200
{txt}{hline 78}
{help ivreg2##widtest:Weak identification test} (Cragg-Donald Wald F statistic):{res}{col 71}   0.805
{txt}                         (Kleibergen-Paap rk Wald F statistic):{res}{col 71}   0.807
{txt}Stock-Yogo weak ID test critical values:{res}{txt}{col 42} 5% maximal IV relative bias{res}{col 73} 18.73
{txt}{col 42}10% maximal IV relative bias{res}{col 73} 10.33
{txt}{col 42}20% maximal IV relative bias{res}{col 73}  5.94
{txt}{col 42}30% maximal IV relative bias{res}{col 73}  4.37
{txt}Source: Stock-Yogo (2005).  Reproduced by permission.
NB: Critical values are for Cragg-Donald F statistic and i.i.d. errors.
{hline 78}
{help ivreg2##overidtests:Hansen J statistic} (overidentification test of all instruments):{res}{col 71}   7.050
{txt}{col 52}Chi-sq({res}12{txt}) P-val =  {res}{col 73}0.8543
{txt}{hline 78}
Instrumented:{col 23}satis_head serious_h_csqc serious_e_csqc
Excluded instruments:{col 23}TH1_TE1 TH1_TE2 TH1_TE3 TH1_TE4 TH2_TE1 TH2_TE2 TH2_TE3
{col 23}TH2_TE4 TH3_TE1 TH3_TE2 TH3_TE3 TH3_TE4 TH4_TE1 TH4_TE2
{col 23}TH4_TE3
{hline 78}
({res}est4{txt} stored)

{com}. 
. estimates store mod4
{txt}
{com}. estadd scalar fstat = e(widstat)

{txt}added scalar:
              e(fstat) =  {res}.80666662
{txt}
{com}. estadd local CFE "X", replace

{txt}added macro:
                e(CFE) : "{res:X}"

{com}. estadd local Controls "X", replace

{txt}added macro:
           e(Controls) : "{res:X}"

{com}. estadd local IV "16 IVs"

{txt}added macro:
                 e(IV) : "{res:16 IVs}"

{com}. estadd local nothing nothing2 = " "

{txt}added macro:
            e(nothing) : "{res:nothing2 = " "}"

{com}. get_mean        

{txt}added scalar:
                 e(av) =  {res}.5
{txt}
{com}. estadd scalar hausman = `temp_hausman'

{txt}added scalar:
            e(hausman) =  {res}1
{txt}
{com}. local coef4 = _b[satis_head]
{txt}
{com}. local se4 = _se[satis_head]
{txt}
{com}. 
. * Z-test 
. local z_value = abs(`coef4'-`coef2')/sqrt(`se4'^2+`se2'^2)
{txt}
{com}. di (1-normal(`z_value'))*2
{res}.72796257
{txt}
{com}. estadd scalar wald = (1-normal(`z_value'))*2

{txt}added scalar:
               e(wald) =  {res}.72796257
{txt}
{com}. 
. local labgen: variable label satis_dem 
{txt}
{com}. 
. esttab mod1 mod2 mod3 mod4 using "3_output/2_OA/Tables/TableE16.tex", depvar keep(satis_head serious_h_csqc serious_e_csqc ) ///
>         order(satis_head serious_h_csqc serious_e_csqc ) label replace ///
>         cells(b(star fmt(%15.3fc)) se(par fmt(%15.3fc))) interaction(" $\times$ ") ///
>         star(* 0.10 ** 0.05 *** 0.01) ///
>         stats(Controls CFE N av IV fstat hausman wald, layout(@ @ @ @ @ @ @ @) fmt(%15s %15s %15.0fc %9.3f %9.3f %9.3f %9.3f %9.3f %9.3f) ///
>         labels("Individual controls" "Country FE" Observations "Outcome mean" "Instruments" "Cragg-Donald statistic" "Hausman test p-value" "Z-test p-value")) ///
>         collabels(none) mlabels(none) ///
>         mgroups("`labgen'" , pattern(1 0 0 0) ///
>         prefix(\multicolumn{c -(}@span{c )-}{c -(}c{c )-}{c -(}) suffix({c )-}) span erepeat(\cmidrule(lr){c -(}@span{c )-}))
{res}{txt}(output written to {browse  `"3_output/2_OA/Tables/TableE16.tex"'})

{com}.         
.         
. **# Table E.17: Impact on support for democracy, additional regressors - 2SLS.
. * Hausman test for following regression
. qui ivreg2 democ ///
>         (satis_head = TH1_TE1 TH1_TE2 TH1_TE3 TH1_TE4 TH2_TE1 TH2_TE2 TH2_TE3 TH2_TE4 TH3_TE1 TH3_TE2 TH3_TE3 TH3_TE4 TH4_TE1 TH4_TE2 TH4_TE3), small
{txt}
{com}. estimates store mod1b
{txt}
{com}. 
. eststo clear
{txt}
{com}. 
. /* E.17. Col.1. Initial coefficient: 16 instruments */
. * to get the Cragg-Donald statistic: 
. eststo: ivreg2 democ ///
>         (satis_head = TH1_TE1 TH1_TE2 TH1_TE3 TH1_TE4 TH2_TE1 TH2_TE2 TH2_TE3 TH2_TE4 TH3_TE1 TH3_TE2 TH3_TE3 TH3_TE4 TH4_TE1 TH4_TE2 TH4_TE3), small robust
{res}
{txt}IV (2SLS) estimation
{hline 20}

Estimates efficient for homoskedasticity only
Statistics robust to heteroskedasticity

{col 55}Number of obs = {res}   22537
{txt}{col 55}F(  1, 22535) = {res}    0.02
{txt}{col 55}Prob > F      = {res}  0.8762
{txt}Total (centered) SS     = {res} 1987.655944{txt}{col 55}Centered R2   = {res}  0.0022
{txt}Total (uncentered) SS   = {res}       20334{txt}{col 55}Uncentered R2 = {res}  0.9025
{txt}Residual SS             = {res} 1983.237445{txt}{col 55}Root MSE      = {res}   .2967

{txt}{hline 13}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 14}{c |}{col 26}    Robust
{col 1}       democ{col 14}{c |} Coefficient{col 26}  std. err.{col 38}      t{col 46}   P>|t|{col 54}     [95% con{col 67}f. interval]
{hline 13}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 2}satis_head {c |}{col 14}{res}{space 2} .0337519{col 26}{space 2}  .216718{col 37}{space 1}    0.16{col 46}{space 3}0.876{col 54}{space 4}-.3910303{col 67}{space 3} .4585342
{txt}{space 7}_cons {c |}{col 14}{res}{space 2} .8867837{col 26}{space 2} .0993297{col 37}{space 1}    8.93{col 46}{space 3}0.000{col 54}{space 4} .6920906{col 67}{space 3} 1.081477
{txt}{hline 13}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{help ivreg2##idtest:Underidentification test} (Kleibergen-Paap rk LM statistic):{res}{col 71}  19.879
{txt}{col 52}Chi-sq({res}15{txt}) P-val =  {res}{col 73}0.1766
{txt}{hline 78}
{help ivreg2##widtest:Weak identification test} (Cragg-Donald Wald F statistic):{res}{col 71}   1.320
{txt}                         (Kleibergen-Paap rk Wald F statistic):{res}{col 71}   1.330
{txt}Stock-Yogo weak ID test critical values:{res}{txt}{col 42} 5% maximal IV relative bias{res}{col 73} 21.23
{txt}{col 42}10% maximal IV relative bias{res}{col 73} 11.51
{txt}{col 42}20% maximal IV relative bias{res}{col 73}  6.42
{txt}{col 42}30% maximal IV relative bias{res}{col 73}  4.63
{txt}{col 42}10% maximal IV size{res}{col 73} 50.39
{txt}{col 42}15% maximal IV size{res}{col 73} 26.80
{txt}{col 42}20% maximal IV size{res}{col 73} 18.72
{txt}{col 42}25% maximal IV size{res}{col 73} 14.60
{txt}Source: Stock-Yogo (2005).  Reproduced by permission.
NB: Critical values are for Cragg-Donald F statistic and i.i.d. errors.
{hline 78}
{help ivreg2##overidtests:Hansen J statistic} (overidentification test of all instruments):{res}{col 71}  11.772
{txt}{col 52}Chi-sq({res}14{txt}) P-val =  {res}{col 73}0.6246
{txt}{hline 78}
Instrumented:{col 23}satis_head
Excluded instruments:{col 23}TH1_TE1 TH1_TE2 TH1_TE3 TH1_TE4 TH2_TE1 TH2_TE2 TH2_TE3
{col 23}TH2_TE4 TH3_TE1 TH3_TE2 TH3_TE3 TH3_TE4 TH4_TE1 TH4_TE2
{col 23}TH4_TE3
{hline 78}
({res}est1{txt} stored)

{com}. 
. estimates store mod1
{txt}
{com}. estadd scalar fstat = e(widstat)

{txt}added scalar:
              e(fstat) =  {res}1.3295479
{txt}
{com}. estadd local IV "16 IVs"

{txt}added macro:
                 e(IV) : "{res:16 IVs}"

{com}. estadd local nothing nothing2 = " "

{txt}added macro:
            e(nothing) : "{res:nothing2 = " "}"

{com}. get_mean

{txt}added scalar:
                 e(av) =  {res}.902
{txt}
{com}. local coef1 = _b[satis_head]
{txt}
{com}. local se1 = _se[satis_head]
{txt}
{com}.         
. * Hausman test for following regression
. qui ivreg2 democ ///
>         (satis_head = TH1_TE1 TH1_TE2 TH1_TE3 TH1_TE4 TH2_TE1 TH2_TE2 TH2_TE3 TH2_TE4 TH3_TE1 TH3_TE2 TH3_TE3 TH3_TE4 TH4_TE1 TH4_TE2 TH4_TE3) $controls $mv_controls i.country, small
{txt}
{com}. estimates store mod2b
{txt}
{com}. 
. /* E.17. Col.2. Initial coefficient: 16 instruments + controls */
. * to get the Cragg-Donald statistic: 
. eststo: ivreg2 democ ///
>         (satis_head = TH1_TE1 TH1_TE2 TH1_TE3 TH1_TE4 TH2_TE1 TH2_TE2 TH2_TE3 TH2_TE4 TH3_TE1 TH3_TE2 TH3_TE3 TH3_TE4 TH4_TE1 TH4_TE2 TH4_TE3) $controls $mv_controls i.country, small robust
{txt}Warning - collinearities detected
Vars dropped:{col 21}mv_thirties mv_fourties mv_fifties mv_sixties mv_seventies
{col 21}mv_income2quartile mv_income3quartile mv_income4quartile
{col 21}mv_female mv_college mv_christiannotcatholic mv_catholic
{col 21}mv_fulltimeworker mv_unemployed mv_outofLF mv_black
{col 21}mv_latino mv_asian mv_bluecollar mv_serviceworker 4.country
{col 21}7.country 9.country 11.country 12.country
{res}
{txt}IV (2SLS) estimation
{hline 20}

Estimates efficient for homoskedasticity only
Statistics robust to heteroskedasticity

{col 55}Number of obs = {res}   22537
{txt}{col 55}F( 43, 22493) = {res}   18.80
{txt}{col 55}Prob > F      = {res}  0.0000
{txt}Total (centered) SS     = {res} 1987.655944{txt}{col 55}Centered R2   = {res}  0.0392
{txt}Total (uncentered) SS   = {res}       20334{txt}{col 55}Uncentered R2 = {res}  0.9061
{txt}Residual SS             = {res} 1909.785059{txt}{col 55}Root MSE      = {res}   .2914

{txt}{hline 24}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 25}{c |}{col 37}    Robust
{col 1}                  democ{col 25}{c |} Coefficient{col 37}  std. err.{col 49}      t{col 57}   P>|t|{col 65}     [95% con{col 78}f. interval]
{hline 24}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 13}satis_head {c |}{col 25}{res}{space 2} .1023982{col 37}{space 2} .2083611{col 48}{space 1}    0.49{col 57}{space 3}0.623{col 65}{space 4} -.306004{col 78}{space 3} .5108004
{txt}{space 15}thirties {c |}{col 25}{res}{space 2} .0060911{col 37}{space 2} .0083963{col 48}{space 1}    0.73{col 57}{space 3}0.468{col 65}{space 4}-.0103662{col 78}{space 3} .0225484
{txt}{space 15}fourties {c |}{col 25}{res}{space 2} .0163058{col 37}{space 2}  .010126{col 48}{space 1}    1.61{col 57}{space 3}0.107{col 65}{space 4}-.0035418{col 78}{space 3} .0361535
{txt}{space 16}fifties {c |}{col 25}{res}{space 2} .0462606{col 37}{space 2}  .010371{col 48}{space 1}    4.46{col 57}{space 3}0.000{col 65}{space 4} .0259328{col 78}{space 3} .0665884
{txt}{space 16}sixties {c |}{col 25}{res}{space 2} .0689589{col 37}{space 2} .0105067{col 48}{space 1}    6.56{col 57}{space 3}0.000{col 65}{space 4}  .048365{col 78}{space 3} .0895528
{txt}{space 14}seventies {c |}{col 25}{res}{space 2} .0840891{col 37}{space 2} .0085161{col 48}{space 1}    9.87{col 57}{space 3}0.000{col 65}{space 4} .0673969{col 78}{space 3} .1007813
{txt}{space 8}income2quartile {c |}{col 25}{res}{space 2} .0281968{col 37}{space 2} .0071727{col 48}{space 1}    3.93{col 57}{space 3}0.000{col 65}{space 4} .0141378{col 78}{space 3} .0422558
{txt}{space 8}income3quartile {c |}{col 25}{res}{space 2} .0469364{col 37}{space 2} .0077142{col 48}{space 1}    6.08{col 57}{space 3}0.000{col 65}{space 4} .0318161{col 78}{space 3} .0620567
{txt}{space 8}income4quartile {c |}{col 25}{res}{space 2} .0509931{col 37}{space 2} .0103655{col 48}{space 1}    4.92{col 57}{space 3}0.000{col 65}{space 4}  .030676{col 78}{space 3} .0713102
{txt}{space 9}incomenoanswer {c |}{col 25}{res}{space 2} .0085015{col 37}{space 2} .0098195{col 48}{space 1}    0.87{col 57}{space 3}0.387{col 65}{space 4}-.0107453{col 78}{space 3} .0277484
{txt}{space 17}female {c |}{col 25}{res}{space 2}-.0101964{col 37}{space 2} .0045093{col 48}{space 1}   -2.26{col 57}{space 3}0.024{col 65}{space 4} -.019035{col 78}{space 3}-.0013578
{txt}{space 13}highschool {c |}{col 25}{res}{space 2} .0239526{col 37}{space 2} .0075669{col 48}{space 1}    3.17{col 57}{space 3}0.002{col 65}{space 4}  .009121{col 78}{space 3} .0387843
{txt}{space 16}college {c |}{col 25}{res}{space 2} .0670183{col 37}{space 2} .0066756{col 48}{space 1}   10.04{col 57}{space 3}0.000{col 65}{space 4} .0539336{col 78}{space 3}  .080103
{txt}{space 13}noreligion {c |}{col 25}{res}{space 2} .0394337{col 37}{space 2} .0116877{col 48}{space 1}    3.37{col 57}{space 3}0.001{col 65}{space 4} .0165249{col 78}{space 3} .0623424
{txt}{space 3}christiannotcatholic {c |}{col 25}{res}{space 2} .0293499{col 37}{space 2} .0172298{col 48}{space 1}    1.70{col 57}{space 3}0.088{col 65}{space 4}-.0044217{col 78}{space 3} .0631216
{txt}{space 15}catholic {c |}{col 25}{res}{space 2} .0354713{col 37}{space 2} .0109128{col 48}{space 1}    3.25{col 57}{space 3}0.001{col 65}{space 4} .0140814{col 78}{space 3} .0568612
{txt}{space 9}fulltimeworker {c |}{col 25}{res}{space 2} .0166043{col 37}{space 2}  .065966{col 48}{space 1}    0.25{col 57}{space 3}0.801{col 65}{space 4}-.1126938{col 78}{space 3} .1459023
{txt}{space 9}parttimeworker {c |}{col 25}{res}{space 2} .0424428{col 37}{space 2} .0677941{col 48}{space 1}    0.63{col 57}{space 3}0.531{col 65}{space 4}-.0904383{col 78}{space 3}  .175324
{txt}{space 13}unemployed {c |}{col 25}{res}{space 2}  .012916{col 37}{space 2} .0746332{col 48}{space 1}    0.17{col 57}{space 3}0.863{col 65}{space 4}-.1333702{col 78}{space 3} .1592022
{txt}{space 11}selfemployed {c |}{col 25}{res}{space 2} .0137822{col 37}{space 2} .0825596{col 48}{space 1}    0.17{col 57}{space 3}0.867{col 65}{space 4}-.1480404{col 78}{space 3} .1756047
{txt}{space 16}outofLF {c |}{col 25}{res}{space 2}  .029226{col 37}{space 2} .0687087{col 48}{space 1}    0.43{col 57}{space 3}0.671{col 65}{space 4}-.1054478{col 78}{space 3} .1638997
{txt}{space 13}goodhealth {c |}{col 25}{res}{space 2} .0274864{col 37}{space 2} .0113374{col 48}{space 1}    2.42{col 57}{space 3}0.015{col 65}{space 4} .0052643{col 78}{space 3} .0497084
{txt}{space 18}white {c |}{col 25}{res}{space 2} .0506319{col 37}{space 2} .0648562{col 48}{space 1}    0.78{col 57}{space 3}0.435{col 65}{space 4}-.0764907{col 78}{space 3} .1777545
{txt}{space 18}black {c |}{col 25}{res}{space 2}  .069515{col 37}{space 2} .0642737{col 48}{space 1}    1.08{col 57}{space 3}0.279{col 65}{space 4} -.056466{col 78}{space 3}  .195496
{txt}{space 17}latino {c |}{col 25}{res}{space 2} .0965484{col 37}{space 2} .0684786{col 48}{space 1}    1.41{col 57}{space 3}0.159{col 65}{space 4}-.0376744{col 78}{space 3} .2307712
{txt}{space 18}asian {c |}{col 25}{res}{space 2} .0816873{col 37}{space 2} .0690595{col 48}{space 1}    1.18{col 57}{space 3}0.237{col 65}{space 4}-.0536741{col 78}{space 3} .2170487
{txt}{space 12}whitecollar {c |}{col 25}{res}{space 2}-.0120083{col 37}{space 2} .0102888{col 48}{space 1}   -1.17{col 57}{space 3}0.243{col 65}{space 4}-.0321751{col 78}{space 3} .0081585
{txt}{space 13}bluecollar {c |}{col 25}{res}{space 2}-.0207131{col 37}{space 2} .0123368{col 48}{space 1}   -1.68{col 57}{space 3}0.093{col 65}{space 4}-.0448941{col 78}{space 3}  .003468
{txt}{space 10}serviceworker {c |}{col 25}{res}{space 2}-.0112827{col 37}{space 2} .0086907{col 48}{space 1}   -1.30{col 57}{space 3}0.194{col 65}{space 4}-.0283171{col 78}{space 3} .0057517
{txt}{space 12}mv_thirties {c |}{col 25}{res}{space 2}        0{col 37}{txt}  (omitted)
{space 12}mv_fourties {c |}{col 25}{res}{space 2}        0{col 37}{txt}  (omitted)
{space 13}mv_fifties {c |}{col 25}{res}{space 2}        0{col 37}{txt}  (omitted)
{space 13}mv_sixties {c |}{col 25}{res}{space 2}        0{col 37}{txt}  (omitted)
{space 11}mv_seventies {c |}{col 25}{res}{space 2}        0{col 37}{txt}  (omitted)
{space 5}mv_income2quartile {c |}{col 25}{res}{space 2}        0{col 37}{txt}  (omitted)
{space 5}mv_income3quartile {c |}{col 25}{res}{space 2}        0{col 37}{txt}  (omitted)
{space 5}mv_income4quartile {c |}{col 25}{res}{space 2}        0{col 37}{txt}  (omitted)
{space 6}mv_incomenoanswer {c |}{col 25}{res}{space 2} .0005538{col 37}{space 2} .0199732{col 48}{space 1}    0.03{col 57}{space 3}0.978{col 65}{space 4}-.0385949{col 78}{space 3} .0397026
{txt}{space 14}mv_female {c |}{col 25}{res}{space 2}        0{col 37}{txt}  (omitted)
{space 10}mv_highschool {c |}{col 25}{res}{space 2} .0251337{col 37}{space 2} .0175051{col 48}{space 1}    1.44{col 57}{space 3}0.151{col 65}{space 4}-.0091775{col 78}{space 3} .0594449
{txt}{space 13}mv_college {c |}{col 25}{res}{space 2}        0{col 37}{txt}  (omitted)
{space 10}mv_noreligion {c |}{col 25}{res}{space 2} .0317691{col 37}{space 2} .0266812{col 48}{space 1}    1.19{col 57}{space 3}0.234{col 65}{space 4} -.020528{col 78}{space 3} .0840662
{txt}mv_christiannotcatholic {c |}{col 25}{res}{space 2}        0{col 37}{txt}  (omitted)
{space 12}mv_catholic {c |}{col 25}{res}{space 2}        0{col 37}{txt}  (omitted)
{space 6}mv_fulltimeworker {c |}{col 25}{res}{space 2}        0{col 37}{txt}  (omitted)
{space 6}mv_parttimeworker {c |}{col 25}{res}{space 2}   .03905{col 37}{space 2} .0641943{col 48}{space 1}    0.61{col 57}{space 3}0.543{col 65}{space 4}-.0867752{col 78}{space 3} .1648753
{txt}{space 10}mv_unemployed {c |}{col 25}{res}{space 2}        0{col 37}{txt}  (omitted)
{space 8}mv_selfemployed {c |}{col 25}{res}{space 2} -.066599{col 37}{space 2} .0657347{col 48}{space 1}   -1.01{col 57}{space 3}0.311{col 65}{space 4}-.1954436{col 78}{space 3} .0622457
{txt}{space 13}mv_outofLF {c |}{col 25}{res}{space 2}        0{col 37}{txt}  (omitted)
{space 10}mv_goodhealth {c |}{col 25}{res}{space 2}-.1839394{col 37}{space 2}  .141094{col 48}{space 1}   -1.30{col 57}{space 3}0.192{col 65}{space 4}-.4604934{col 78}{space 3} .0926146
{txt}{space 15}mv_white {c |}{col 25}{res}{space 2} .1285453{col 37}{space 2} .0988659{col 48}{space 1}    1.30{col 57}{space 3}0.194{col 65}{space 4}-.0652387{col 78}{space 3} .3223293
{txt}{space 15}mv_black {c |}{col 25}{res}{space 2}        0{col 37}{txt}  (omitted)
{space 14}mv_latino {c |}{col 25}{res}{space 2}        0{col 37}{txt}  (omitted)
{space 15}mv_asian {c |}{col 25}{res}{space 2}        0{col 37}{txt}  (omitted)
{space 9}mv_whitecollar {c |}{col 25}{res}{space 2}-.0705549{col 37}{space 2} .0668946{col 48}{space 1}   -1.05{col 57}{space 3}0.292{col 65}{space 4}-.2016729{col 78}{space 3} .0605631
{txt}{space 10}mv_bluecollar {c |}{col 25}{res}{space 2}        0{col 37}{txt}  (omitted)
{space 7}mv_serviceworker {c |}{col 25}{res}{space 2}        0{col 37}{txt}  (omitted)
{space 23} {c |}
{space 16}country {c |}
{space 15}Austria  {c |}{col 25}{res}{space 2} .1204074{col 37}{space 2}  .124201{col 48}{space 1}    0.97{col 57}{space 3}0.332{col 65}{space 4}-.1230353{col 78}{space 3} .3638501
{txt}{space 16}Brazil  {c |}{col 25}{res}{space 2}-.0698866{col 37}{space 2} .0217084{col 48}{space 1}   -3.22{col 57}{space 3}0.001{col 65}{space 4}-.1124365{col 78}{space 3}-.0273366
{txt}{space 16}France  {c |}{col 25}{res}{space 2}        0{col 37}{txt}  (omitted)
{space 15}Germany  {c |}{col 25}{res}{space 2}  .062125{col 37}{space 2} .1312827{col 48}{space 1}    0.47{col 57}{space 3}0.636{col 65}{space 4}-.1951981{col 78}{space 3} .3194482
{txt}{space 17}Italy  {c |}{col 25}{res}{space 2} .1044462{col 37}{space 2} .1223026{col 48}{space 1}    0.85{col 57}{space 3}0.393{col 65}{space 4}-.1352754{col 78}{space 3} .3441678
{txt}{space 11}New_Zealand  {c |}{col 25}{res}{space 2}        0{col 37}{txt}  (omitted)
{space 16}Poland  {c |}{col 25}{res}{space 2} -.105539{col 37}{space 2} .0233474{col 48}{space 1}   -4.52{col 57}{space 3}0.000{col 65}{space 4}-.1513015{col 78}{space 3}-.0597766
{txt}{space 17}Spain  {c |}{col 25}{res}{space 2}        0{col 37}{txt}  (omitted)
{space 16}Sweden  {c |}{col 25}{res}{space 2} -.066631{col 37}{space 2} .0239689{col 48}{space 1}   -2.78{col 57}{space 3}0.005{col 65}{space 4}-.1136117{col 78}{space 3}-.0196502
{txt}{space 20}UK  {c |}{col 25}{res}{space 2}        0{col 37}{txt}  (omitted)
{space 20}US  {c |}{col 25}{res}{space 2}        0{col 37}{txt}  (omitted)
{space 23} {c |}
{space 18}_cons {c |}{col 25}{res}{space 2} .6636018{col 37}{space 2} .0633164{col 48}{space 1}   10.48{col 57}{space 3}0.000{col 65}{space 4} .5394972{col 78}{space 3} .7877065
{txt}{hline 24}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{help ivreg2##idtest:Underidentification test} (Kleibergen-Paap rk LM statistic):{res}{col 71}  23.663
{txt}{col 52}Chi-sq({res}15{txt}) P-val =  {res}{col 73}0.0710
{txt}{hline 78}
{help ivreg2##widtest:Weak identification test} (Cragg-Donald Wald F statistic):{res}{col 71}   1.551
{txt}                         (Kleibergen-Paap rk Wald F statistic):{res}{col 71}   1.580
{txt}Stock-Yogo weak ID test critical values:{res}{txt}{col 42} 5% maximal IV relative bias{res}{col 73} 21.23
{txt}{col 42}10% maximal IV relative bias{res}{col 73} 11.51
{txt}{col 42}20% maximal IV relative bias{res}{col 73}  6.42
{txt}{col 42}30% maximal IV relative bias{res}{col 73}  4.63
{txt}{col 42}10% maximal IV size{res}{col 73} 50.39
{txt}{col 42}15% maximal IV size{res}{col 73} 26.80
{txt}{col 42}20% maximal IV size{res}{col 73} 18.72
{txt}{col 42}25% maximal IV size{res}{col 73} 14.60
{txt}Source: Stock-Yogo (2005).  Reproduced by permission.
NB: Critical values are for Cragg-Donald F statistic and i.i.d. errors.
{hline 78}
{help ivreg2##overidtests:Hansen J statistic} (overidentification test of all instruments):{res}{col 71}  13.744
{txt}{col 52}Chi-sq({res}14{txt}) P-val =  {res}{col 73}0.4690
{txt}{hline 78}
Instrumented:{col 23}satis_head
Included instruments:{col 23}thirties fourties fifties sixties seventies
{col 23}income2quartile income3quartile income4quartile
{col 23}incomenoanswer female highschool college noreligion
{col 23}christiannotcatholic catholic fulltimeworker
{col 23}parttimeworker unemployed selfemployed outofLF goodhealth
{col 23}white black latino asian whitecollar bluecollar
{col 23}serviceworker mv_incomenoanswer mv_highschool
{col 23}mv_noreligion mv_parttimeworker mv_selfemployed
{col 23}mv_goodhealth mv_white mv_whitecollar 2.country 3.country
{col 23}5.country 6.country 8.country 10.country
Excluded instruments:{col 23}TH1_TE1 TH1_TE2 TH1_TE3 TH1_TE4 TH2_TE1 TH2_TE2 TH2_TE3
{col 23}TH2_TE4 TH3_TE1 TH3_TE2 TH3_TE3 TH3_TE4 TH4_TE1 TH4_TE2
{col 23}TH4_TE3
Dropped collinear:{col 23}mv_thirties mv_fourties mv_fifties mv_sixties
{col 23}mv_seventies mv_income2quartile mv_income3quartile
{col 23}mv_income4quartile mv_female mv_college
{col 23}mv_christiannotcatholic mv_catholic mv_fulltimeworker
{col 23}mv_unemployed mv_outofLF mv_black mv_latino mv_asian
{col 23}mv_bluecollar mv_serviceworker 4.country 7.country
{col 23}9.country 11.country 12.country
{hline 78}
({res}est2{txt} stored)

{com}. 
. estimates store mod2
{txt}
{com}. estadd scalar fstat = e(widstat)

{txt}added scalar:
              e(fstat) =  {res}1.5801279
{txt}
{com}. estadd local CFE "X", replace

{txt}added macro:
                e(CFE) : "{res:X}"

{com}. estadd local Controls "X", replace

{txt}added macro:
           e(Controls) : "{res:X}"

{com}. estadd local IV "16 IVs"

{txt}added macro:
                 e(IV) : "{res:16 IVs}"

{com}. estadd local nothing nothing2 = " "

{txt}added macro:
            e(nothing) : "{res:nothing2 = " "}"

{com}. get_mean

{txt}added scalar:
                 e(av) =  {res}.902
{txt}
{com}. local coef2 = _b[satis_head]
{txt}
{com}. local se2 = _se[satis_head]
{txt}
{com}. 
. * Hausman test for following regression
. qui ivreg2 democ ///
>         (satis_head serious_h_csqc serious_e_csqc = TH1_TE1 TH1_TE2 TH1_TE3 TH1_TE4 TH2_TE1 TH2_TE2 TH2_TE3 TH2_TE4 TH3_TE1 TH3_TE2 TH3_TE3 TH3_TE4 TH4_TE1 TH4_TE2 TH4_TE3), small 
{txt}
{com}. estimates store mod3b
{txt}
{com}. 
. hausman mod3b mod1b

{txt}{col 18}{hline 4} Coefficients {hline 4}
{col 14}{c |}{col 21}(b){col 34}(B){col 49}(b-B){col 59}sqrt(diag(V_b-V_B))
{col 14}{c |}{col 17}   mod3b    {col 30}   mod1b    {col 46}Difference{col 63}Std. err.
{hline 13}{c +}{hline 64}
{space 2}satis_head {c |}{res}{col 18}-.1315211{col 31} .0337519{col 47} -.165273{col 63} .1884676
{txt}{hline 13}{c BT}{hline 64}
{ralign 78:b = Consistent under H0 and Ha; obtained from {bf:ivreg2}.}
{ralign 78:B = Inconsistent under Ha, efficient under H0; obtained from {bf:ivreg2}.}

Test of H0: Difference in coefficients not systematic

{ralign 11:chi2({res:1})} = (b-B)'[(V_b-V_B)^(-1)](b-B)
{ralign 11:} = {res:{ralign 6:0.77}}
{ralign 11:Prob > chi2} = {res:{ralign 6:0.3805}}

{com}. local temp_hausman = r(p)
{txt}
{com}. 
. /* E.17. Col.3. 16 instruments + additional endogenous variables */
. * to get the Cragg-Donald statistic: 
. eststo: ivreg2 democ ///
>         (satis_head serious_h_csqc serious_e_csqc = TH1_TE1 TH1_TE2 TH1_TE3 TH1_TE4 TH2_TE1 TH2_TE2 TH2_TE3 TH2_TE4 TH3_TE1 TH3_TE2 TH3_TE3 TH3_TE4 TH4_TE1 TH4_TE2 TH4_TE3), small robust
{res}
{txt}IV (2SLS) estimation
{hline 20}

Estimates efficient for homoskedasticity only
Statistics robust to heteroskedasticity

{col 55}Number of obs = {res}   22533
{txt}{col 55}F(  3, 22529) = {res}    0.34
{txt}{col 55}Prob > F      = {res}  0.7995
{txt}Total (centered) SS     = {res} 1987.617716{txt}{col 55}Centered R2   = {res} -0.0350
{txt}Total (uncentered) SS   = {res}       20330{txt}{col 55}Uncentered R2 = {res}  0.8988
{txt}Residual SS             = {res} 2057.243924{txt}{col 55}Root MSE      = {res}   .3022

{txt}{hline 15}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 16}{c |}{col 28}    Robust
{col 1}         democ{col 16}{c |} Coefficient{col 28}  std. err.{col 40}      t{col 48}   P>|t|{col 56}     [95% con{col 69}f. interval]
{hline 15}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 4}satis_head {c |}{col 16}{res}{space 2}-.1315211{col 28}{space 2} .2885158{col 39}{space 1}   -0.46{col 48}{space 3}0.648{col 56}{space 4}-.6970321{col 69}{space 3} .4339899
{txt}serious_h_csqc {c |}{col 16}{res}{space 2}-.0209054{col 28}{space 2} .0626903{col 39}{space 1}   -0.33{col 48}{space 3}0.739{col 56}{space 4}-.1437827{col 69}{space 3} .1019719
{txt}serious_e_csqc {c |}{col 16}{res}{space 2} -.048033{col 28}{space 2} .0530356{col 39}{space 1}   -0.91{col 48}{space 3}0.365{col 56}{space 4}-.1519864{col 69}{space 3} .0559204
{txt}{space 9}_cons {c |}{col 16}{res}{space 2} .9853574{col 28}{space 2} .1484946{col 39}{space 1}    6.64{col 48}{space 3}0.000{col 56}{space 4} .6942978{col 69}{space 3} 1.276417
{txt}{hline 15}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{help ivreg2##idtest:Underidentification test} (Kleibergen-Paap rk LM statistic):{res}{col 71}  12.155
{txt}{col 52}Chi-sq({res}13{txt}) P-val =  {res}{col 73}0.5150
{txt}{hline 78}
{help ivreg2##widtest:Weak identification test} (Cragg-Donald Wald F statistic):{res}{col 71}   0.809
{txt}                         (Kleibergen-Paap rk Wald F statistic):{res}{col 71}   0.811
{txt}Stock-Yogo weak ID test critical values:{res}{txt}{col 42} 5% maximal IV relative bias{res}{col 73} 18.73
{txt}{col 42}10% maximal IV relative bias{res}{col 73} 10.33
{txt}{col 42}20% maximal IV relative bias{res}{col 73}  5.94
{txt}{col 42}30% maximal IV relative bias{res}{col 73}  4.37
{txt}Source: Stock-Yogo (2005).  Reproduced by permission.
NB: Critical values are for Cragg-Donald F statistic and i.i.d. errors.
{hline 78}
{help ivreg2##overidtests:Hansen J statistic} (overidentification test of all instruments):{res}{col 71}  10.439
{txt}{col 52}Chi-sq({res}12{txt}) P-val =  {res}{col 73}0.5775
{txt}{hline 78}
Instrumented:{col 23}satis_head serious_h_csqc serious_e_csqc
Excluded instruments:{col 23}TH1_TE1 TH1_TE2 TH1_TE3 TH1_TE4 TH2_TE1 TH2_TE2 TH2_TE3
{col 23}TH2_TE4 TH3_TE1 TH3_TE2 TH3_TE3 TH3_TE4 TH4_TE1 TH4_TE2
{col 23}TH4_TE3
{hline 78}
({res}est3{txt} stored)

{com}. 
. estimates store mod3
{txt}
{com}. estadd scalar fstat = e(widstat)

{txt}added scalar:
              e(fstat) =  {res}.81082629
{txt}
{com}. estadd local IV "16 IVs"

{txt}added macro:
                 e(IV) : "{res:16 IVs}"

{com}. estadd local nothing nothing2 = " "

{txt}added macro:
            e(nothing) : "{res:nothing2 = " "}"

{com}. get_mean

{txt}added scalar:
                 e(av) =  {res}.902
{txt}
{com}. estadd scalar hausman = `temp_hausman'

{txt}added scalar:
            e(hausman) =  {res}.38052433
{txt}
{com}. 
. local coef3 = _b[satis_head]
{txt}
{com}. local se3 = _se[satis_head]
{txt}
{com}. 
. * Z-test 
. local z_value = abs(`coef3'-`coef1')/sqrt(`se3'^2+`se1'^2)
{txt}
{com}. di (1-normal(`z_value'))*2
{res}.64693915
{txt}
{com}. estadd scalar wald = (1-normal(`z_value'))*2

{txt}added scalar:
               e(wald) =  {res}.64693915
{txt}
{com}. 
. * Hausman test for following regression
. qui ivreg2 democ ///
>         (satis_head serious_h_csqc serious_e_csqc = TH1_TE1 TH1_TE2 TH1_TE3 TH1_TE4 TH2_TE1 TH2_TE2 TH2_TE3 TH2_TE4 TH3_TE1 TH3_TE2 TH3_TE3 TH3_TE4 TH4_TE1 TH4_TE2 TH4_TE3) $controls $mv_controls i.country, small 
{txt}
{com}. estimates store mod4b
{txt}
{com}. hausman mod4b mod2b

{txt}{col 18}{hline 4} Coefficients {hline 4}
{col 14}{c |}{col 21}(b){col 34}(B){col 49}(b-B){col 59}sqrt(diag(V_b-V_B))
{col 14}{c |}{col 17}   mod4b    {col 30}   mod2b    {col 46}Difference{col 63}Std. err.
{hline 13}{c +}{hline 64}
{space 2}satis_head {c |}{res}{col 18}-.0309054{col 31} .1023982{col 47}-.1333036{col 63} .1673561
{txt}{space 4}thirties {c |}{res}{col 18} .0051597{col 31} .0060911{col 47}-.0009314{col 63} .0013707
{txt}{space 4}fourties {c |}{res}{col 18} .0143728{col 31} .0163058{col 47} -.001933{col 63}  .003339
{txt}{space 5}fifties {c |}{res}{col 18} .0448764{col 31} .0462606{col 47}-.0013842{col 63} .0030296
{txt}{space 5}sixties {c |}{res}{col 18} .0669415{col 31} .0689589{col 47}-.0020174{col 63} .0038223
{txt}{space 3}seventies {c |}{res}{col 18} .0853391{col 31} .0840891{col 47} .0012499{col 63} .0026742
{txt}income2qua~e {c |}{res}{col 18} .0297511{col 31} .0281968{col 47} .0015543{col 63} .0019113
{txt}income3qua~e {c |}{res}{col 18} .0488391{col 31} .0469364{col 47} .0019026{col 63} .0022061
{txt}income4qua~e {c |}{res}{col 18} .0555886{col 31} .0509931{col 47} .0045955{col 63}  .005469
{txt}incomenoan~r {c |}{res}{col 18} .0068371{col 31} .0085015{col 47}-.0016645{col 63} .0021658
{txt}{space 6}female {c |}{res}{col 18}-.0079857{col 31}-.0101964{col 47} .0022107{col 63} .0031566
{txt}{space 2}highschool {c |}{res}{col 18} .0250972{col 31} .0239526{col 47} .0011445{col 63} .0014459
{txt}{space 5}college {c |}{res}{col 18} .0672418{col 31} .0670183{col 47} .0002235{col 63} .0015163
{txt}{space 2}noreligion {c |}{res}{col 18} .0345746{col 31} .0394337{col 47}-.0048591{col 63} .0070538
{txt}christiann~c {c |}{res}{col 18}  .036659{col 31} .0293499{col 47}  .007309{col 63}  .008797
{txt}{space 4}catholic {c |}{res}{col 18} .0388941{col 31} .0354713{col 47} .0034228{col 63}  .004049
{txt}fulltimewo~r {c |}{res}{col 18}-.0242402{col 31} .0166043{col 47}-.0408444{col 63} .0513076
{txt}parttimewo~r {c |}{res}{col 18} .0038093{col 31} .0424428{col 47}-.0386336{col 63} .0489021
{txt}{space 2}unemployed {c |}{res}{col 18}-.0312548{col 31}  .012916{col 47}-.0441708{col 63} .0564767
{txt}selfemployed {c |}{res}{col 18}-.0358182{col 31} .0137822{col 47}-.0496004{col 63} .0632059
{txt}{space 5}outofLF {c |}{res}{col 18}-.0128423{col 31}  .029226{col 47}-.0420683{col 63} .0532209
{txt}{space 2}goodhealth {c |}{res}{col 18} .0338481{col 31} .0274864{col 47} .0063618{col 63}   .00746
{txt}{space 7}white {c |}{res}{col 18} .0684941{col 31} .0506319{col 47} .0178622{col 63} .0232402
{txt}{space 7}black {c |}{res}{col 18} .0791056{col 31}  .069515{col 47} .0095906{col 63} .0154666
{txt}{space 6}latino {c |}{res}{col 18} .1048103{col 31} .0965484{col 47} .0082619{col 63} .0137611
{txt}{space 7}asian {c |}{res}{col 18} .0904236{col 31} .0816873{col 47} .0087363{col 63} .0119204
{txt}{space 1}whitecollar {c |}{res}{col 18}-.0128066{col 31}-.0120083{col 47}-.0007983{col 63} .0014277
{txt}{space 2}bluecollar {c |}{res}{col 18} -.022609{col 31}-.0207131{col 47}-.0018959{col 63} .0032681
{txt}servicewor~r {c |}{res}{col 18}-.0119612{col 31}-.0112827{col 47}-.0006785{col 63} .0026041
{txt}mv_incomen~r {c |}{res}{col 18} .0002434{col 31} .0005538{col 47}-.0003104{col 63} .0015651
{txt}mv_highsch~l {c |}{res}{col 18} .0243341{col 31} .0251337{col 47}-.0007996{col 63} .0020956
{txt}mv_norelig~n {c |}{res}{col 18} .0337866{col 31} .0317691{col 47} .0020175{col 63} .0030699
{txt}mv_parttim~r {c |}{res}{col 18} .0020383{col 31}   .03905{col 47}-.0370118{col 63} .0469986
{txt}mv_selfemp~d {c |}{res}{col 18}-.0435451{col 31} -.066599{col 47} .0230539{col 63} .0301863
{txt}mv_goodhea~h {c |}{res}{col 18}-.1517078{col 31}-.1839394{col 47} .0322317{col 63} .0400213
{txt}{space 4}mv_white {c |}{res}{col 18} .1781275{col 31} .1285453{col 47} .0495822{col 63} .0609331
{txt}mv_whiteco~r {c |}{res}{col 18} -.043641{col 31}-.0705549{col 47} .0269139{col 63} .0329938
{txt}{space 5}country {c |}
{space 10}2  {c |}{res}{col 18}  .170854{col 31} .1204074{col 47} .0504466{col 63} .0608444
{txt}{space 10}3  {c |}{res}{col 18}-.0645367{col 31}-.0698866{col 47} .0053499{col 63} .0121858
{txt}{space 10}5  {c |}{res}{col 18}  .117665{col 31}  .062125{col 47}   .05554{col 63} .0677512
{txt}{space 10}6  {c |}{res}{col 18} .1598374{col 31} .1044462{col 47} .0553912{col 63} .0655712
{txt}{space 10}8  {c |}{res}{col 18}-.1244208{col 31} -.105539{col 47}-.0188818{col 63} .0246383
{txt}{space 9}10  {c |}{res}{col 18}-.0723017{col 31} -.066631{col 47}-.0056707{col 63} .0071967
{txt}{hline 13}{c BT}{hline 64}
{ralign 78:b = Consistent under H0 and Ha; obtained from {bf:ivreg2}.}
{ralign 78:B = Inconsistent under Ha, efficient under H0; obtained from {bf:ivreg2}.}

Test of H0: Difference in coefficients not systematic

{ralign 11:chi2({res:43})} = (b-B)'[(V_b-V_B)^(-1)](b-B)
{ralign 11:} = {res:{ralign 6:0.85}}
{ralign 11:Prob > chi2} = {res:{ralign 6:1.0000}}
(V_b-V_B is not positive definite)

{com}. local temp_hausman = r(p)
{txt}
{com}.  
. /* E.17. Col.4. 16 instruments + additional endogenous variables + controls */
. * to get the Cragg-Donald statistic: 
. eststo: ivreg2 democ ///
>         (satis_head serious_h_csqc serious_e_csqc = TH1_TE1 TH1_TE2 TH1_TE3 TH1_TE4 TH2_TE1 TH2_TE2 TH2_TE3 TH2_TE4 TH3_TE1 TH3_TE2 TH3_TE3 TH3_TE4 TH4_TE1 TH4_TE2 TH4_TE3), small robust
{res}
{txt}IV (2SLS) estimation
{hline 20}

Estimates efficient for homoskedasticity only
Statistics robust to heteroskedasticity

{col 55}Number of obs = {res}   22533
{txt}{col 55}F(  3, 22529) = {res}    0.34
{txt}{col 55}Prob > F      = {res}  0.7995
{txt}Total (centered) SS     = {res} 1987.617716{txt}{col 55}Centered R2   = {res} -0.0350
{txt}Total (uncentered) SS   = {res}       20330{txt}{col 55}Uncentered R2 = {res}  0.8988
{txt}Residual SS             = {res} 2057.243924{txt}{col 55}Root MSE      = {res}   .3022

{txt}{hline 15}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 16}{c |}{col 28}    Robust
{col 1}         democ{col 16}{c |} Coefficient{col 28}  std. err.{col 40}      t{col 48}   P>|t|{col 56}     [95% con{col 69}f. interval]
{hline 15}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 4}satis_head {c |}{col 16}{res}{space 2}-.1315211{col 28}{space 2} .2885158{col 39}{space 1}   -0.46{col 48}{space 3}0.648{col 56}{space 4}-.6970321{col 69}{space 3} .4339899
{txt}serious_h_csqc {c |}{col 16}{res}{space 2}-.0209054{col 28}{space 2} .0626903{col 39}{space 1}   -0.33{col 48}{space 3}0.739{col 56}{space 4}-.1437827{col 69}{space 3} .1019719
{txt}serious_e_csqc {c |}{col 16}{res}{space 2} -.048033{col 28}{space 2} .0530356{col 39}{space 1}   -0.91{col 48}{space 3}0.365{col 56}{space 4}-.1519864{col 69}{space 3} .0559204
{txt}{space 9}_cons {c |}{col 16}{res}{space 2} .9853574{col 28}{space 2} .1484946{col 39}{space 1}    6.64{col 48}{space 3}0.000{col 56}{space 4} .6942978{col 69}{space 3} 1.276417
{txt}{hline 15}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{help ivreg2##idtest:Underidentification test} (Kleibergen-Paap rk LM statistic):{res}{col 71}  12.155
{txt}{col 52}Chi-sq({res}13{txt}) P-val =  {res}{col 73}0.5150
{txt}{hline 78}
{help ivreg2##widtest:Weak identification test} (Cragg-Donald Wald F statistic):{res}{col 71}   0.809
{txt}                         (Kleibergen-Paap rk Wald F statistic):{res}{col 71}   0.811
{txt}Stock-Yogo weak ID test critical values:{res}{txt}{col 42} 5% maximal IV relative bias{res}{col 73} 18.73
{txt}{col 42}10% maximal IV relative bias{res}{col 73} 10.33
{txt}{col 42}20% maximal IV relative bias{res}{col 73}  5.94
{txt}{col 42}30% maximal IV relative bias{res}{col 73}  4.37
{txt}Source: Stock-Yogo (2005).  Reproduced by permission.
NB: Critical values are for Cragg-Donald F statistic and i.i.d. errors.
{hline 78}
{help ivreg2##overidtests:Hansen J statistic} (overidentification test of all instruments):{res}{col 71}  10.439
{txt}{col 52}Chi-sq({res}12{txt}) P-val =  {res}{col 73}0.5775
{txt}{hline 78}
Instrumented:{col 23}satis_head serious_h_csqc serious_e_csqc
Excluded instruments:{col 23}TH1_TE1 TH1_TE2 TH1_TE3 TH1_TE4 TH2_TE1 TH2_TE2 TH2_TE3
{col 23}TH2_TE4 TH3_TE1 TH3_TE2 TH3_TE3 TH3_TE4 TH4_TE1 TH4_TE2
{col 23}TH4_TE3
{hline 78}
({res}est4{txt} stored)

{com}. 
. estimates store mod4
{txt}
{com}. estadd scalar fstat = e(widstat)

{txt}added scalar:
              e(fstat) =  {res}.81082629
{txt}
{com}. estadd local CFE "X", replace

{txt}added macro:
                e(CFE) : "{res:X}"

{com}. estadd local Controls "X", replace

{txt}added macro:
           e(Controls) : "{res:X}"

{com}. estadd local IV "16 IVs"

{txt}added macro:
                 e(IV) : "{res:16 IVs}"

{com}. estadd local nothing nothing2 = " "

{txt}added macro:
            e(nothing) : "{res:nothing2 = " "}"

{com}. get_mean        

{txt}added scalar:
                 e(av) =  {res}.902
{txt}
{com}. estadd scalar hausman = `temp_hausman'

{txt}added scalar:
            e(hausman) =  {res}1
{txt}
{com}. local coef4 = _b[satis_head]
{txt}
{com}. local se4 = _se[satis_head]
{txt}
{com}. 
. * Z-test 
. local z_value = abs(`coef4'-`coef2')/sqrt(`se4'^2+`se2'^2)
{txt}
{com}. di (1-normal(`z_value'))*2
{res}.51099781
{txt}
{com}. estadd scalar wald = (1-normal(`z_value'))*2

{txt}added scalar:
               e(wald) =  {res}.51099781
{txt}
{com}. 
. local labgen: variable label democ 
{txt}
{com}. 
. esttab mod1 mod2 mod3 mod4 using "3_output/2_OA/Tables/TableE17.tex", depvar keep(satis_head serious_h_csqc serious_e_csqc ) ///
>         order(satis_head serious_h_csqc serious_e_csqc ) label replace ///
>         cells(b(star fmt(%15.3fc)) se(par fmt(%15.3fc))) interaction(" $\times$ ") ///
>         star(* 0.10 ** 0.05 *** 0.01) ///
>         stats(Controls CFE N av IV fstat hausman wald, layout(@ @ @ @ @ @ @ @) fmt(%15s %15s %15.0fc %9.3f %9.3f %9.3f %9.3f %9.3f %9.3f) ///
>         labels("Individual controls" "Country FE" Observations "Outcome mean" "Instruments" "Cragg-Donald statistic" "Hausman test p-value" "Z-test p-value")) ///
>         collabels(none) mlabels(none) ///
>         mgroups("`labgen'" , pattern(1 0 0 0) ///
>         prefix(\multicolumn{c -(}@span{c )-}{c -(}c{c )-}{c -(}) suffix({c )-}) span erepeat(\cmidrule(lr){c -(}@span{c )-}))
{res}{txt}(output written to {browse  `"3_output/2_OA/Tables/TableE17.tex"'})

{com}. 
. 
. 
. /* *************************************************************************** */
. /* E.7. Robusness to controlling for satisfaction for the regional government */
. /* *************************************************************************** */
. 
. **** question not asked in countries where the survey was administered by CSA, we have to drop Australia and the US.
. preserve
{txt}
{com}. keep if country_str!= "Australia" & country_str!= "US"
{txt}(3,016 observations deleted)

{com}. 
. **# Table E.18: Impact of the health and economic consequences of the crisis on satisfaction with the regional government.
. eststo clear
{txt}
{com}. eststo: reg satis_reg bad_health_situation bad_econ_situation , robust

{txt}Linear regression                               Number of obs     = {res}    19,523
                                                {txt}F(2, 19520)       =  {res}     3.22
                                                {txt}Prob > F          = {res}    0.0400
                                                {txt}R-squared         = {res}    0.0003
                                                {txt}Root MSE          =    {res} .24755

{txt}{hline 21}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 22}{c |}{col 34}    Robust
{col 1}           satis_reg{col 22}{c |} Coefficient{col 34}  std. err.{col 46}      t{col 54}   P>|t|{col 62}     [95% con{col 75}f. interval]
{hline 21}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
bad_health_situation {c |}{col 22}{res}{space 2}-.0079565{col 34}{space 2} .0035432{col 45}{space 1}   -2.25{col 54}{space 3}0.025{col 62}{space 4}-.0149015{col 75}{space 3}-.0010115
{txt}{space 2}bad_econ_situation {c |}{col 22}{res}{space 2}-.0041963{col 34}{space 2} .0035434{col 45}{space 1}   -1.18{col 54}{space 3}0.236{col 62}{space 4}-.0111416{col 75}{space 3}  .002749
{txt}{space 15}_cons {c |}{col 22}{res}{space 2} .5719709{col 34}{space 2} .0030619{col 45}{space 1}  186.80{col 54}{space 3}0.000{col 62}{space 4} .5659693{col 75}{space 3} .5779726
{txt}{hline 21}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{res}{txt}({res}est1{txt} stored)

{com}. get_mean

{txt}added scalar:
                 e(av) =  {res}.566
{txt}
{com}. eststo: reg satis_reg bad_health_situation bad_econ_situation $controls $mv_controls i.country, robust
{txt}{p 0 6 2}note: {bf:white} omitted because of collinearity.{p_end}
{p 0 6 2}note: {bf:black} omitted because of collinearity.{p_end}
{p 0 6 2}note: {bf:latino} omitted because of collinearity.{p_end}
{p 0 6 2}note: {bf:asian} omitted because of collinearity.{p_end}
{p 0 6 2}note: {bf:mv_thirties} omitted because of collinearity.{p_end}
{p 0 6 2}note: {bf:mv_fourties} omitted because of collinearity.{p_end}
{p 0 6 2}note: {bf:mv_fifties} omitted because of collinearity.{p_end}
{p 0 6 2}note: {bf:mv_sixties} omitted because of collinearity.{p_end}
{p 0 6 2}note: {bf:mv_seventies} omitted because of collinearity.{p_end}
{p 0 6 2}note: {bf:mv_income2quartile} omitted because of collinearity.{p_end}
{p 0 6 2}note: {bf:mv_income3quartile} omitted because of collinearity.{p_end}
{p 0 6 2}note: {bf:mv_income4quartile} omitted because of collinearity.{p_end}
{p 0 6 2}note: {bf:mv_incomenoanswer} omitted because of collinearity.{p_end}
{p 0 6 2}note: {bf:mv_female} omitted because of collinearity.{p_end}
{p 0 6 2}note: {bf:mv_college} omitted because of collinearity.{p_end}
{p 0 6 2}note: {bf:mv_christiannotcatholic} omitted because of collinearity.{p_end}
{p 0 6 2}note: {bf:mv_catholic} omitted because of collinearity.{p_end}
{p 0 6 2}note: {bf:mv_fulltimeworker} omitted because of collinearity.{p_end}
{p 0 6 2}note: {bf:mv_unemployed} omitted because of collinearity.{p_end}
{p 0 6 2}note: {bf:mv_selfemployed} omitted because of collinearity.{p_end}
{p 0 6 2}note: {bf:mv_outofLF} omitted because of collinearity.{p_end}
{p 0 6 2}note: {bf:mv_black} omitted because of collinearity.{p_end}
{p 0 6 2}note: {bf:mv_latino} omitted because of collinearity.{p_end}
{p 0 6 2}note: {bf:mv_asian} omitted because of collinearity.{p_end}
{p 0 6 2}note: {bf:mv_bluecollar} omitted because of collinearity.{p_end}
{p 0 6 2}note: {bf:mv_serviceworker} omitted because of collinearity.{p_end}
{p 0 6 2}note: {bf:4.country} omitted because of collinearity.{p_end}
{p 0 6 2}note: {bf:7.country} omitted because of collinearity.{p_end}
{p 0 6 2}note: {bf:9.country} omitted because of collinearity.{p_end}
{p 0 6 2}note: {bf:11.country} omitted because of collinearity.{p_end}

Linear regression                               Number of obs     = {res}    19,523
                                                {txt}{help j_robustsingular:F(36, 19485) }     =  {res}        .
                                                {txt}Prob > F          = {res}         .
                                                {txt}R-squared         = {res}    0.0646
                                                {txt}Root MSE          =    {res} .23968

{txt}{hline 24}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 25}{c |}{col 37}    Robust
{col 1}              satis_reg{col 25}{c |} Coefficient{col 37}  std. err.{col 49}      t{col 57}   P>|t|{col 65}     [95% con{col 78}f. interval]
{hline 24}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 3}bad_health_situation {c |}{col 25}{res}{space 2}-.0083877{col 37}{space 2}  .003433{col 48}{space 1}   -2.44{col 57}{space 3}0.015{col 65}{space 4}-.0151167{col 78}{space 3}-.0016587
{txt}{space 5}bad_econ_situation {c |}{col 25}{res}{space 2}-.0046885{col 37}{space 2} .0034307{col 48}{space 1}   -1.37{col 57}{space 3}0.172{col 65}{space 4} -.011413{col 78}{space 3} .0020359
{txt}{space 15}thirties {c |}{col 25}{res}{space 2} -.015747{col 37}{space 2} .0064027{col 48}{space 1}   -2.46{col 57}{space 3}0.014{col 65}{space 4}-.0282969{col 78}{space 3}-.0031971
{txt}{space 15}fourties {c |}{col 25}{res}{space 2}-.0271543{col 37}{space 2} .0062803{col 48}{space 1}   -4.32{col 57}{space 3}0.000{col 65}{space 4}-.0394642{col 78}{space 3}-.0148444
{txt}{space 16}fifties {c |}{col 25}{res}{space 2}-.0211277{col 37}{space 2} .0064388{col 48}{space 1}   -3.28{col 57}{space 3}0.001{col 65}{space 4}-.0337483{col 78}{space 3}-.0085071
{txt}{space 16}sixties {c |}{col 25}{res}{space 2}-.0097621{col 37}{space 2} .0064486{col 48}{space 1}   -1.51{col 57}{space 3}0.130{col 65}{space 4} -.022402{col 78}{space 3} .0028778
{txt}{space 14}seventies {c |}{col 25}{res}{space 2} .0054034{col 37}{space 2} .0074596{col 48}{space 1}    0.72{col 57}{space 3}0.469{col 65}{space 4} -.009218{col 78}{space 3} .0200249
{txt}{space 8}income2quartile {c |}{col 25}{res}{space 2} .0223038{col 37}{space 2} .0051342{col 48}{space 1}    4.34{col 57}{space 3}0.000{col 65}{space 4} .0122404{col 78}{space 3} .0323672
{txt}{space 8}income3quartile {c |}{col 25}{res}{space 2} .0193411{col 37}{space 2} .0053406{col 48}{space 1}    3.62{col 57}{space 3}0.000{col 65}{space 4}  .008873{col 78}{space 3} .0298091
{txt}{space 8}income4quartile {c |}{col 25}{res}{space 2} .0143808{col 37}{space 2} .0054695{col 48}{space 1}    2.63{col 57}{space 3}0.009{col 65}{space 4}   .00366{col 78}{space 3} .0251016
{txt}{space 9}incomenoanswer {c |}{col 25}{res}{space 2}-.0194597{col 37}{space 2} .0081947{col 48}{space 1}   -2.37{col 57}{space 3}0.018{col 65}{space 4}-.0355221{col 78}{space 3}-.0033973
{txt}{space 17}female {c |}{col 25}{res}{space 2} .0179201{col 37}{space 2} .0035394{col 48}{space 1}    5.06{col 57}{space 3}0.000{col 65}{space 4} .0109825{col 78}{space 3} .0248577
{txt}{space 13}highschool {c |}{col 25}{res}{space 2} -.009575{col 37}{space 2} .0057172{col 48}{space 1}   -1.67{col 57}{space 3}0.094{col 65}{space 4}-.0207812{col 78}{space 3} .0016312
{txt}{space 16}college {c |}{col 25}{res}{space 2}-.0057595{col 37}{space 2} .0052551{col 48}{space 1}   -1.10{col 57}{space 3}0.273{col 65}{space 4}-.0160599{col 78}{space 3} .0045409
{txt}{space 13}noreligion {c |}{col 25}{res}{space 2} .0106768{col 37}{space 2} .0085074{col 48}{space 1}    1.26{col 57}{space 3}0.209{col 65}{space 4}-.0059984{col 78}{space 3}  .027352
{txt}{space 3}christiannotcatholic {c |}{col 25}{res}{space 2} .0448823{col 37}{space 2} .0100343{col 48}{space 1}    4.47{col 57}{space 3}0.000{col 65}{space 4} .0252142{col 78}{space 3} .0645503
{txt}{space 15}catholic {c |}{col 25}{res}{space 2} .0453374{col 37}{space 2} .0085958{col 48}{space 1}    5.27{col 57}{space 3}0.000{col 65}{space 4} .0284889{col 78}{space 3} .0621859
{txt}{space 9}fulltimeworker {c |}{col 25}{res}{space 2}-.0759984{col 37}{space 2} .0080846{col 48}{space 1}   -9.40{col 57}{space 3}0.000{col 65}{space 4} -.091845{col 78}{space 3}-.0601519
{txt}{space 9}parttimeworker {c |}{col 25}{res}{space 2}-.0762897{col 37}{space 2} .0243221{col 48}{space 1}   -3.14{col 57}{space 3}0.002{col 65}{space 4} -.123963{col 78}{space 3}-.0286163
{txt}{space 13}unemployed {c |}{col 25}{res}{space 2}-.0876715{col 37}{space 2} .0127369{col 48}{space 1}   -6.88{col 57}{space 3}0.000{col 65}{space 4} -.112637{col 78}{space 3} -.062706
{txt}{space 11}selfemployed {c |}{col 25}{res}{space 2}-.0505657{col 37}{space 2} .0326686{col 48}{space 1}   -1.55{col 57}{space 3}0.122{col 65}{space 4} -.114599{col 78}{space 3} .0134676
{txt}{space 16}outofLF {c |}{col 25}{res}{space 2}-.0694876{col 37}{space 2} .0087319{col 48}{space 1}   -7.96{col 57}{space 3}0.000{col 65}{space 4}-.0866029{col 78}{space 3}-.0523723
{txt}{space 13}goodhealth {c |}{col 25}{res}{space 2} .0463524{col 37}{space 2} .0037437{col 48}{space 1}   12.38{col 57}{space 3}0.000{col 65}{space 4} .0390144{col 78}{space 3} .0536904
{txt}{space 18}white {c |}{col 25}{res}{space 2}        0{col 37}{txt}  (omitted)
{space 18}black {c |}{col 25}{res}{space 2}        0{col 37}{txt}  (omitted)
{space 17}latino {c |}{col 25}{res}{space 2}        0{col 37}{txt}  (omitted)
{space 18}asian {c |}{col 25}{res}{space 2}        0{col 37}{txt}  (omitted)
{space 12}whitecollar {c |}{col 25}{res}{space 2}  .002374{col 37}{space 2} .0091123{col 48}{space 1}    0.26{col 57}{space 3}0.794{col 65}{space 4} -.015487{col 78}{space 3} .0202349
{txt}{space 13}bluecollar {c |}{col 25}{res}{space 2}-.0138137{col 37}{space 2}  .009442{col 48}{space 1}   -1.46{col 57}{space 3}0.143{col 65}{space 4}-.0323209{col 78}{space 3} .0046935
{txt}{space 10}serviceworker {c |}{col 25}{res}{space 2}-.0007154{col 37}{space 2}  .007577{col 48}{space 1}   -0.09{col 57}{space 3}0.925{col 65}{space 4}-.0155669{col 78}{space 3} .0141362
{txt}{space 12}mv_thirties {c |}{col 25}{res}{space 2}        0{col 37}{txt}  (omitted)
{space 12}mv_fourties {c |}{col 25}{res}{space 2}        0{col 37}{txt}  (omitted)
{space 13}mv_fifties {c |}{col 25}{res}{space 2}        0{col 37}{txt}  (omitted)
{space 13}mv_sixties {c |}{col 25}{res}{space 2}        0{col 37}{txt}  (omitted)
{space 11}mv_seventies {c |}{col 25}{res}{space 2}        0{col 37}{txt}  (omitted)
{space 5}mv_income2quartile {c |}{col 25}{res}{space 2}        0{col 37}{txt}  (omitted)
{space 5}mv_income3quartile {c |}{col 25}{res}{space 2}        0{col 37}{txt}  (omitted)
{space 5}mv_income4quartile {c |}{col 25}{res}{space 2}        0{col 37}{txt}  (omitted)
{space 6}mv_incomenoanswer {c |}{col 25}{res}{space 2}        0{col 37}{txt}  (omitted)
{space 14}mv_female {c |}{col 25}{res}{space 2}        0{col 37}{txt}  (omitted)
{space 10}mv_highschool {c |}{col 25}{res}{space 2}  .012098{col 37}{space 2}  .013488{col 48}{space 1}    0.90{col 57}{space 3}0.370{col 65}{space 4}-.0143397{col 78}{space 3} .0385357
{txt}{space 13}mv_college {c |}{col 25}{res}{space 2}        0{col 37}{txt}  (omitted)
{space 10}mv_noreligion {c |}{col 25}{res}{space 2}  .047429{col 37}{space 2} .0264485{col 48}{space 1}    1.79{col 57}{space 3}0.073{col 65}{space 4}-.0044123{col 78}{space 3} .0992702
{txt}mv_christiannotcatholic {c |}{col 25}{res}{space 2}        0{col 37}{txt}  (omitted)
{space 12}mv_catholic {c |}{col 25}{res}{space 2}        0{col 37}{txt}  (omitted)
{space 6}mv_fulltimeworker {c |}{col 25}{res}{space 2}        0{col 37}{txt}  (omitted)
{space 6}mv_parttimeworker {c |}{col 25}{res}{space 2} .1136231{col 37}{space 2} .0085185{col 48}{space 1}   13.34{col 57}{space 3}0.000{col 65}{space 4} .0969262{col 78}{space 3} .1303201
{txt}{space 10}mv_unemployed {c |}{col 25}{res}{space 2}        0{col 37}{txt}  (omitted)
{space 8}mv_selfemployed {c |}{col 25}{res}{space 2}        0{col 37}{txt}  (omitted)
{space 13}mv_outofLF {c |}{col 25}{res}{space 2}        0{col 37}{txt}  (omitted)
{space 10}mv_goodhealth {c |}{col 25}{res}{space 2} .2340044{col 37}{space 2} .0123696{col 48}{space 1}   18.92{col 57}{space 3}0.000{col 65}{space 4} .2097589{col 78}{space 3} .2582499
{txt}{space 15}mv_white {c |}{col 25}{res}{space 2} -.088901{col 37}{space 2}  .017199{col 48}{space 1}   -5.17{col 57}{space 3}0.000{col 65}{space 4}-.1226125{col 78}{space 3}-.0551895
{txt}{space 15}mv_black {c |}{col 25}{res}{space 2}        0{col 37}{txt}  (omitted)
{space 14}mv_latino {c |}{col 25}{res}{space 2}        0{col 37}{txt}  (omitted)
{space 15}mv_asian {c |}{col 25}{res}{space 2}        0{col 37}{txt}  (omitted)
{space 9}mv_whitecollar {c |}{col 25}{res}{space 2} .1190639{col 37}{space 2} .0173209{col 48}{space 1}    6.87{col 57}{space 3}0.000{col 65}{space 4} .0851134{col 78}{space 3} .1530144
{txt}{space 10}mv_bluecollar {c |}{col 25}{res}{space 2}        0{col 37}{txt}  (omitted)
{space 7}mv_serviceworker {c |}{col 25}{res}{space 2}        0{col 37}{txt}  (omitted)
{space 23} {c |}
{space 16}country {c |}
{space 16}Brazil  {c |}{col 25}{res}{space 2}-.0882655{col 37}{space 2} .0183022{col 48}{space 1}   -4.82{col 57}{space 3}0.000{col 65}{space 4}-.1241394{col 78}{space 3}-.0523916
{txt}{space 16}France  {c |}{col 25}{res}{space 2}        0{col 37}{txt}  (omitted)
{space 15}Germany  {c |}{col 25}{res}{space 2}-.0116887{col 37}{space 2} .0096958{col 48}{space 1}   -1.21{col 57}{space 3}0.228{col 65}{space 4}-.0306932{col 78}{space 3} .0073158
{txt}{space 17}Italy  {c |}{col 25}{res}{space 2} -.126159{col 37}{space 2} .0277845{col 48}{space 1}   -4.54{col 57}{space 3}0.000{col 65}{space 4} -.180619{col 78}{space 3} -.071699
{txt}{space 11}New_Zealand  {c |}{col 25}{res}{space 2}        0{col 37}{txt}  (omitted)
{space 16}Poland  {c |}{col 25}{res}{space 2} -.013141{col 37}{space 2} .0177307{col 48}{space 1}   -0.74{col 57}{space 3}0.459{col 65}{space 4}-.0478948{col 78}{space 3} .0216128
{txt}{space 17}Spain  {c |}{col 25}{res}{space 2}        0{col 37}{txt}  (omitted)
{space 16}Sweden  {c |}{col 25}{res}{space 2}-.0253172{col 37}{space 2} .0174755{col 48}{space 1}   -1.45{col 57}{space 3}0.147{col 65}{space 4}-.0595706{col 78}{space 3} .0089362
{txt}{space 20}UK  {c |}{col 25}{res}{space 2}        0{col 37}{txt}  (omitted)
{space 23} {c |}
{space 18}_cons {c |}{col 25}{res}{space 2} .4597766{col 37}{space 2}  .016641{col 48}{space 1}   27.63{col 57}{space 3}0.000{col 65}{space 4} .4271587{col 78}{space 3} .4923944
{txt}{hline 24}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{res}{txt}({res}est2{txt} stored)

{com}. estadd local Controls "X", replace

{txt}added macro:
           e(Controls) : "{res:X}"

{com}. estadd local Country_FE "X", replace

{txt}added macro:
         e(Country_FE) : "{res:X}"

{com}. get_mean

{txt}added scalar:
                 e(av) =  {res}.566
{txt}
{com}. esttab using "3_output/2_OA/Tables/TableE18.tex", r2 dep nocons ///
>         keep(_cons bad_health_situation bad_econ_situation ) ///
>         order(_cons bad_health_situation bad_econ_situation ) ///
>         label replace   cells(b(star fmt(%15.3fc)) se(par fmt(%15.3fc))) interaction(" $\times$ ") ///
>         star(* 0.10 ** 0.05 *** 0.01) ///
>         stats(Controls Country_FE N r2 av, fmt(%15.0fc %15.0fc %15.0fc %6.3f %6.3f ) layout(@ @ @ @ @) ///
>         labels("Individual controls" "Country FE" Observations R2 "Outcome mean")) ///
>         collabels(none) mlabels(none) ///
>         mgroups("Satisfaction with the regional government" , pattern(1 0 ) prefix(\multicolumn{c -(}@span{c )-}{c -(}c{c )-}{c -(}) suffix({c )-}) span erepeat(\cmidrule(lr){c -(}@span{c )-})) 
{res}{txt}(output written to {browse  `"3_output/2_OA/Tables/TableE18.tex"'})

{com}. 
.         
. **# Table E.19: Impact of satisfaction with the regional government on satisfaction with democracy.
. eststo clear
{txt}
{com}. /* E.19. Col 1. Initial coefficient: satis_head only */
. * Hausman test for following regression
. qui ivreg2 satis_dem ///
> (satis_head = TH1_TE1 TH1_TE2 TH1_TE3 TH1_TE4 TH2_TE1 TH2_TE2 TH2_TE3 TH2_TE4 TH3_TE1 TH3_TE2 TH3_TE3 TH3_TE4 TH4_TE1 TH4_TE2 TH4_TE3), small
{txt}
{com}. estimates store mod1b
{txt}
{com}. 
. eststo: ivreg2 satis_dem ///
> (satis_head = TH1_TE1 TH1_TE2 TH1_TE3 TH1_TE4 TH2_TE1 TH2_TE2 TH2_TE3 TH2_TE4 TH3_TE1 TH3_TE2 TH3_TE3 TH3_TE4 TH4_TE1 TH4_TE2 TH4_TE3), small robust
{res}
{txt}IV (2SLS) estimation
{hline 20}

Estimates efficient for homoskedasticity only
Statistics robust to heteroskedasticity

{col 55}Number of obs = {res}   19525
{txt}{col 55}F(  1, 19523) = {res}   10.19
{txt}{col 55}Prob > F      = {res}  0.0014
{txt}Total (centered) SS     = {res} 1412.870262{txt}{col 55}Centered R2   = {res}  0.4191
{txt}Total (uncentered) SS   = {res} 6157.490042{txt}{col 55}Uncentered R2 = {res}  0.8667
{txt}Residual SS             = {res} 820.6838686{txt}{col 55}Root MSE      = {res}    .205

{txt}{hline 13}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 14}{c |}{col 26}    Robust
{col 1}   satis_dem{col 14}{c |} Coefficient{col 26}  std. err.{col 38}      t{col 46}   P>|t|{col 54}     [95% con{col 67}f. interval]
{hline 13}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 2}satis_head {c |}{col 14}{res}{space 2} .4968142{col 26}{space 2} .1556584{col 37}{space 1}    3.19{col 46}{space 3}0.001{col 54}{space 4} .1917104{col 67}{space 3}  .801918
{txt}{space 7}_cons {c |}{col 14}{res}{space 2} .2673036{col 26}{space 2} .0707182{col 37}{space 1}    3.78{col 46}{space 3}0.000{col 54}{space 4} .1286898{col 67}{space 3} .4059174
{txt}{hline 13}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{help ivreg2##idtest:Underidentification test} (Kleibergen-Paap rk LM statistic):{res}{col 71}  18.672
{txt}{col 52}Chi-sq({res}15{txt}) P-val =  {res}{col 73}0.2290
{txt}{hline 78}
{help ivreg2##widtest:Weak identification test} (Cragg-Donald Wald F statistic):{res}{col 71}   1.237
{txt}                         (Kleibergen-Paap rk Wald F statistic):{res}{col 71}   1.248
{txt}Stock-Yogo weak ID test critical values:{res}{txt}{col 42} 5% maximal IV relative bias{res}{col 73} 21.23
{txt}{col 42}10% maximal IV relative bias{res}{col 73} 11.51
{txt}{col 42}20% maximal IV relative bias{res}{col 73}  6.42
{txt}{col 42}30% maximal IV relative bias{res}{col 73}  4.63
{txt}{col 42}10% maximal IV size{res}{col 73} 50.39
{txt}{col 42}15% maximal IV size{res}{col 73} 26.80
{txt}{col 42}20% maximal IV size{res}{col 73} 18.72
{txt}{col 42}25% maximal IV size{res}{col 73} 14.60
{txt}Source: Stock-Yogo (2005).  Reproduced by permission.
NB: Critical values are for Cragg-Donald F statistic and i.i.d. errors.
{hline 78}
{help ivreg2##overidtests:Hansen J statistic} (overidentification test of all instruments):{res}{col 71}   7.406
{txt}{col 52}Chi-sq({res}14{txt}) P-val =  {res}{col 73}0.9179
{txt}{hline 78}
Instrumented:{col 23}satis_head
Excluded instruments:{col 23}TH1_TE1 TH1_TE2 TH1_TE3 TH1_TE4 TH2_TE1 TH2_TE2 TH2_TE3
{col 23}TH2_TE4 TH3_TE1 TH3_TE2 TH3_TE3 TH3_TE4 TH4_TE1 TH4_TE2
{col 23}TH4_TE3
{hline 78}
({res}est1{txt} stored)

{com}. 
. estimates store mod1
{txt}
{com}. estadd scalar fstat = e(widstat)

{txt}added scalar:
              e(fstat) =  {res}1.2484755
{txt}
{com}. estadd local IV "16 IVs"

{txt}added macro:
                 e(IV) : "{res:16 IVs}"

{com}. estadd local nothing nothing2 = " "

{txt}added macro:
            e(nothing) : "{res:nothing2 = " "}"

{com}. get_mean

{txt}added scalar:
                 e(av) =  {res}.493
{txt}
{com}. local coef1 = _b[satis_head]
{txt}
{com}. local se1 = _se[satis_head]
{txt}
{com}. 
. /* E.19. Col 2. Initial coefficient: satis_head only + controls */
. * Hausman test for following regression
. qui ivreg2 satis_dem ///
> (satis_head = TH1_TE1 TH1_TE2 TH1_TE3 TH1_TE4 TH2_TE1 TH2_TE2 TH2_TE3 TH2_TE4 TH3_TE1 TH3_TE2 TH3_TE3 TH3_TE4 TH4_TE1 TH4_TE2 TH4_TE3) $controls $mv_controls i.country, small                                                  
{txt}
{com}. estimates store mod2b
{txt}
{com}. 
. eststo: ivreg2 satis_dem ///
> (satis_head = TH1_TE1 TH1_TE2 TH1_TE3 TH1_TE4 TH2_TE1 TH2_TE2 TH2_TE3 TH2_TE4 TH3_TE1 TH3_TE2 TH3_TE3 TH3_TE4 TH4_TE1 TH4_TE2 TH4_TE3) $controls $mv_controls i.country, small robust
{txt}Warning - collinearities detected
Vars dropped:{col 21}white black latino asian mv_thirties mv_fourties mv_fifties
{col 21}mv_sixties mv_seventies mv_income2quartile
{col 21}mv_income3quartile mv_income4quartile mv_incomenoanswer
{col 21}mv_female mv_college mv_christiannotcatholic mv_catholic
{col 21}mv_fulltimeworker mv_unemployed mv_selfemployed mv_outofLF
{col 21}mv_black mv_latino mv_asian mv_bluecollar mv_serviceworker
{col 21}4.country 7.country 9.country 11.country
{res}
{txt}IV (2SLS) estimation
{hline 20}

Estimates efficient for homoskedasticity only
Statistics robust to heteroskedasticity

{col 55}Number of obs = {res}   19525
{txt}{col 55}F( 36, 19488) = {res} 3840.64
{txt}{col 55}Prob > F      = {res}  0.0000
{txt}Total (centered) SS     = {res} 1412.870262{txt}{col 55}Centered R2   = {res}  0.4546
{txt}Total (uncentered) SS   = {res} 6157.490042{txt}{col 55}Uncentered R2 = {res}  0.8749
{txt}Residual SS             = {res} 770.5984013{txt}{col 55}Root MSE      = {res}   .1989

{txt}{hline 24}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 25}{c |}{col 37}    Robust
{col 1}              satis_dem{col 25}{c |} Coefficient{col 37}  std. err.{col 49}      t{col 57}   P>|t|{col 65}     [95% con{col 78}f. interval]
{hline 24}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 13}satis_head {c |}{col 25}{res}{space 2} .5122516{col 37}{space 2} .1460021{col 48}{space 1}    3.51{col 57}{space 3}0.000{col 65}{space 4} .2260749{col 78}{space 3} .7984282
{txt}{space 15}thirties {c |}{col 25}{res}{space 2}-.0178843{col 37}{space 2} .0056878{col 48}{space 1}   -3.14{col 57}{space 3}0.002{col 65}{space 4}-.0290328{col 78}{space 3}-.0067357
{txt}{space 15}fourties {c |}{col 25}{res}{space 2}-.0139635{col 37}{space 2}  .007369{col 48}{space 1}   -1.89{col 57}{space 3}0.058{col 65}{space 4}-.0284074{col 78}{space 3} .0004804
{txt}{space 16}fifties {c |}{col 25}{res}{space 2}-.0131575{col 37}{space 2} .0074494{col 48}{space 1}   -1.77{col 57}{space 3}0.077{col 65}{space 4} -.027759{col 78}{space 3} .0014441
{txt}{space 16}sixties {c |}{col 25}{res}{space 2} .0000869{col 37}{space 2} .0070664{col 48}{space 1}    0.01{col 57}{space 3}0.990{col 65}{space 4}-.0137638{col 78}{space 3} .0139375
{txt}{space 14}seventies {c |}{col 25}{res}{space 2}  .015896{col 37}{space 2} .0063485{col 48}{space 1}    2.50{col 57}{space 3}0.012{col 65}{space 4} .0034524{col 78}{space 3} .0283396
{txt}{space 8}income2quartile {c |}{col 25}{res}{space 2}  .016901{col 37}{space 2} .0052728{col 48}{space 1}    3.21{col 57}{space 3}0.001{col 65}{space 4} .0065657{col 78}{space 3} .0272362
{txt}{space 8}income3quartile {c |}{col 25}{res}{space 2} .0241417{col 37}{space 2} .0059398{col 48}{space 1}    4.06{col 57}{space 3}0.000{col 65}{space 4} .0124991{col 78}{space 3} .0357842
{txt}{space 8}income4quartile {c |}{col 25}{res}{space 2} .0391394{col 37}{space 2} .0080337{col 48}{space 1}    4.87{col 57}{space 3}0.000{col 65}{space 4} .0233927{col 78}{space 3}  .054886
{txt}{space 9}incomenoanswer {c |}{col 25}{res}{space 2} .0055897{col 37}{space 2} .0067061{col 48}{space 1}    0.83{col 57}{space 3}0.405{col 65}{space 4}-.0075548{col 78}{space 3} .0187342
{txt}{space 17}female {c |}{col 25}{res}{space 2}-.0064257{col 37}{space 2} .0040726{col 48}{space 1}   -1.58{col 57}{space 3}0.115{col 65}{space 4}-.0144084{col 78}{space 3} .0015569
{txt}{space 13}highschool {c |}{col 25}{res}{space 2} .0063657{col 37}{space 2} .0049804{col 48}{space 1}    1.28{col 57}{space 3}0.201{col 65}{space 4}-.0033962{col 78}{space 3} .0161276
{txt}{space 16}college {c |}{col 25}{res}{space 2} .0238042{col 37}{space 2} .0046307{col 48}{space 1}    5.14{col 57}{space 3}0.000{col 65}{space 4} .0147276{col 78}{space 3} .0328808
{txt}{space 13}noreligion {c |}{col 25}{res}{space 2}-.0009456{col 37}{space 2}  .008188{col 48}{space 1}   -0.12{col 57}{space 3}0.908{col 65}{space 4}-.0169947{col 78}{space 3} .0151035
{txt}{space 3}christiannotcatholic {c |}{col 25}{res}{space 2} .0271262{col 37}{space 2} .0113144{col 48}{space 1}    2.40{col 57}{space 3}0.017{col 65}{space 4}  .004949{col 78}{space 3} .0493034
{txt}{space 15}catholic {c |}{col 25}{res}{space 2} .0283307{col 37}{space 2}  .007332{col 48}{space 1}    3.86{col 57}{space 3}0.000{col 65}{space 4} .0139593{col 78}{space 3} .0427021
{txt}{space 9}fulltimeworker {c |}{col 25}{res}{space 2}-.0382945{col 37}{space 2} .0458175{col 48}{space 1}   -0.84{col 57}{space 3}0.403{col 65}{space 4}-.1281008{col 78}{space 3} .0515118
{txt}{space 9}parttimeworker {c |}{col 25}{res}{space 2}-.0032072{col 37}{space 2} .0507113{col 48}{space 1}   -0.06{col 57}{space 3}0.950{col 65}{space 4}-.1026056{col 78}{space 3} .0961912
{txt}{space 13}unemployed {c |}{col 25}{res}{space 2}-.0496231{col 37}{space 2} .0512923{col 48}{space 1}   -0.97{col 57}{space 3}0.333{col 65}{space 4}-.1501604{col 78}{space 3} .0509142
{txt}{space 11}selfemployed {c |}{col 25}{res}{space 2}-.0260848{col 37}{space 2} .0618988{col 48}{space 1}   -0.42{col 57}{space 3}0.673{col 65}{space 4}-.1474117{col 78}{space 3} .0952422
{txt}{space 16}outofLF {c |}{col 25}{res}{space 2}-.0377784{col 37}{space 2} .0484997{col 48}{space 1}   -0.78{col 57}{space 3}0.436{col 65}{space 4}-.1328419{col 78}{space 3} .0572851
{txt}{space 13}goodhealth {c |}{col 25}{res}{space 2} .0252403{col 37}{space 2} .0077084{col 48}{space 1}    3.27{col 57}{space 3}0.001{col 65}{space 4} .0101313{col 78}{space 3} .0403494
{txt}{space 18}white {c |}{col 25}{res}{space 2}        0{col 37}{txt}  (omitted)
{space 18}black {c |}{col 25}{res}{space 2}        0{col 37}{txt}  (omitted)
{space 17}latino {c |}{col 25}{res}{space 2}        0{col 37}{txt}  (omitted)
{space 18}asian {c |}{col 25}{res}{space 2}        0{col 37}{txt}  (omitted)
{space 12}whitecollar {c |}{col 25}{res}{space 2} .0032383{col 37}{space 2} .0076367{col 48}{space 1}    0.42{col 57}{space 3}0.672{col 65}{space 4}-.0117303{col 78}{space 3} .0182069
{txt}{space 13}bluecollar {c |}{col 25}{res}{space 2} -.010353{col 37}{space 2}  .007972{col 48}{space 1}   -1.30{col 57}{space 3}0.194{col 65}{space 4}-.0259788{col 78}{space 3} .0052728
{txt}{space 10}serviceworker {c |}{col 25}{res}{space 2}-.0098787{col 37}{space 2} .0059746{col 48}{space 1}   -1.65{col 57}{space 3}0.098{col 65}{space 4}-.0215894{col 78}{space 3}  .001832
{txt}{space 12}mv_thirties {c |}{col 25}{res}{space 2}        0{col 37}{txt}  (omitted)
{space 12}mv_fourties {c |}{col 25}{res}{space 2}        0{col 37}{txt}  (omitted)
{space 13}mv_fifties {c |}{col 25}{res}{space 2}        0{col 37}{txt}  (omitted)
{space 13}mv_sixties {c |}{col 25}{res}{space 2}        0{col 37}{txt}  (omitted)
{space 11}mv_seventies {c |}{col 25}{res}{space 2}        0{col 37}{txt}  (omitted)
{space 5}mv_income2quartile {c |}{col 25}{res}{space 2}        0{col 37}{txt}  (omitted)
{space 5}mv_income3quartile {c |}{col 25}{res}{space 2}        0{col 37}{txt}  (omitted)
{space 5}mv_income4quartile {c |}{col 25}{res}{space 2}        0{col 37}{txt}  (omitted)
{space 6}mv_incomenoanswer {c |}{col 25}{res}{space 2}        0{col 37}{txt}  (omitted)
{space 14}mv_female {c |}{col 25}{res}{space 2}        0{col 37}{txt}  (omitted)
{space 10}mv_highschool {c |}{col 25}{res}{space 2} .0246177{col 37}{space 2} .0107427{col 48}{space 1}    2.29{col 57}{space 3}0.022{col 65}{space 4} .0035611{col 78}{space 3} .0456743
{txt}{space 13}mv_college {c |}{col 25}{res}{space 2}        0{col 37}{txt}  (omitted)
{space 10}mv_noreligion {c |}{col 25}{res}{space 2} .0106878{col 37}{space 2} .0233557{col 48}{space 1}    0.46{col 57}{space 3}0.647{col 65}{space 4}-.0350914{col 78}{space 3}  .056467
{txt}mv_christiannotcatholic {c |}{col 25}{res}{space 2}        0{col 37}{txt}  (omitted)
{space 12}mv_catholic {c |}{col 25}{res}{space 2}        0{col 37}{txt}  (omitted)
{space 6}mv_fulltimeworker {c |}{col 25}{res}{space 2}        0{col 37}{txt}  (omitted)
{space 6}mv_parttimeworker {c |}{col 25}{res}{space 2} .0164377{col 37}{space 2} .0079995{col 48}{space 1}    2.05{col 57}{space 3}0.040{col 65}{space 4}  .000758{col 78}{space 3} .0321173
{txt}{space 10}mv_unemployed {c |}{col 25}{res}{space 2}        0{col 37}{txt}  (omitted)
{space 8}mv_selfemployed {c |}{col 25}{res}{space 2}        0{col 37}{txt}  (omitted)
{space 13}mv_outofLF {c |}{col 25}{res}{space 2}        0{col 37}{txt}  (omitted)
{space 10}mv_goodhealth {c |}{col 25}{res}{space 2} .5326308{col 37}{space 2} .0197744{col 48}{space 1}   26.94{col 57}{space 3}0.000{col 65}{space 4} .4938713{col 78}{space 3} .5713904
{txt}{space 15}mv_white {c |}{col 25}{res}{space 2} -.012144{col 37}{space 2} .0241867{col 48}{space 1}   -0.50{col 57}{space 3}0.616{col 65}{space 4} -.059552{col 78}{space 3} .0352639
{txt}{space 15}mv_black {c |}{col 25}{res}{space 2}        0{col 37}{txt}  (omitted)
{space 14}mv_latino {c |}{col 25}{res}{space 2}        0{col 37}{txt}  (omitted)
{space 15}mv_asian {c |}{col 25}{res}{space 2}        0{col 37}{txt}  (omitted)
{space 9}mv_whitecollar {c |}{col 25}{res}{space 2}-.0183543{col 37}{space 2} .0464705{col 48}{space 1}   -0.39{col 57}{space 3}0.693{col 65}{space 4}-.1094405{col 78}{space 3} .0727318
{txt}{space 10}mv_bluecollar {c |}{col 25}{res}{space 2}        0{col 37}{txt}  (omitted)
{space 7}mv_serviceworker {c |}{col 25}{res}{space 2}        0{col 37}{txt}  (omitted)
{space 23} {c |}
{space 16}country {c |}
{space 16}Brazil  {c |}{col 25}{res}{space 2} -.074184{col 37}{space 2} .0149292{col 48}{space 1}   -4.97{col 57}{space 3}0.000{col 65}{space 4}-.1034465{col 78}{space 3}-.0449215
{txt}{space 16}France  {c |}{col 25}{res}{space 2}        0{col 37}{txt}  (omitted)
{space 15}Germany  {c |}{col 25}{res}{space 2}-.0215543{col 37}{space 2} .0098959{col 48}{space 1}   -2.18{col 57}{space 3}0.029{col 65}{space 4}-.0409511{col 78}{space 3}-.0021576
{txt}{space 17}Italy  {c |}{col 25}{res}{space 2} -.112316{col 37}{space 2} .0244497{col 48}{space 1}   -4.59{col 57}{space 3}0.000{col 65}{space 4}-.1602394{col 78}{space 3}-.0643925
{txt}{space 11}New_Zealand  {c |}{col 25}{res}{space 2}        0{col 37}{txt}  (omitted)
{space 16}Poland  {c |}{col 25}{res}{space 2}-.0972162{col 37}{space 2}  .014664{col 48}{space 1}   -6.63{col 57}{space 3}0.000{col 65}{space 4}-.1259589{col 78}{space 3}-.0684736
{txt}{space 17}Spain  {c |}{col 25}{res}{space 2}        0{col 37}{txt}  (omitted)
{space 16}Sweden  {c |}{col 25}{res}{space 2} .0648198{col 37}{space 2} .0160121{col 48}{space 1}    4.05{col 57}{space 3}0.000{col 65}{space 4} .0334347{col 78}{space 3}  .096205
{txt}{space 20}UK  {c |}{col 25}{res}{space 2}        0{col 37}{txt}  (omitted)
{space 23} {c |}
{space 18}_cons {c |}{col 25}{res}{space 2} .2464374{col 37}{space 2}   .07502{col 48}{space 1}    3.28{col 57}{space 3}0.001{col 65}{space 4} .0993918{col 78}{space 3} .3934829
{txt}{hline 24}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{help ivreg2##idtest:Underidentification test} (Kleibergen-Paap rk LM statistic):{res}{col 71}  22.479
{txt}{col 52}Chi-sq({res}15{txt}) P-val =  {res}{col 73}0.0959
{txt}{hline 78}
{help ivreg2##widtest:Weak identification test} (Cragg-Donald Wald F statistic):{res}{col 71}   1.477
{txt}                         (Kleibergen-Paap rk Wald F statistic):{res}{col 71}   1.501
{txt}Stock-Yogo weak ID test critical values:{res}{txt}{col 42} 5% maximal IV relative bias{res}{col 73} 21.23
{txt}{col 42}10% maximal IV relative bias{res}{col 73} 11.51
{txt}{col 42}20% maximal IV relative bias{res}{col 73}  6.42
{txt}{col 42}30% maximal IV relative bias{res}{col 73}  4.63
{txt}{col 42}10% maximal IV size{res}{col 73} 50.39
{txt}{col 42}15% maximal IV size{res}{col 73} 26.80
{txt}{col 42}20% maximal IV size{res}{col 73} 18.72
{txt}{col 42}25% maximal IV size{res}{col 73} 14.60
{txt}Source: Stock-Yogo (2005).  Reproduced by permission.
NB: Critical values are for Cragg-Donald F statistic and i.i.d. errors.
{hline 78}
{err}Warning: estimated covariance matrix of moment conditions not of full rank.
         overidentification statistic not reported, and standard errors and
         model tests should be interpreted with caution.
Possible causes:
         singleton dummy variable (dummy with one 1 and N-1 0s or vice versa)
{help ivreg2##partial:partial} option may address problem.
{txt}{hline 78}
Instrumented:{col 23}satis_head
Included instruments:{col 23}thirties fourties fifties sixties seventies
{col 23}income2quartile income3quartile income4quartile
{col 23}incomenoanswer female highschool college noreligion
{col 23}christiannotcatholic catholic fulltimeworker
{col 23}parttimeworker unemployed selfemployed outofLF goodhealth
{col 23}whitecollar bluecollar serviceworker mv_highschool
{col 23}mv_noreligion mv_parttimeworker mv_goodhealth mv_white
{col 23}mv_whitecollar 3.country 5.country 6.country 8.country
{col 23}10.country
Excluded instruments:{col 23}TH1_TE1 TH1_TE2 TH1_TE3 TH1_TE4 TH2_TE1 TH2_TE2 TH2_TE3
{col 23}TH2_TE4 TH3_TE1 TH3_TE2 TH3_TE3 TH3_TE4 TH4_TE1 TH4_TE2
{col 23}TH4_TE3
Dropped collinear:{col 23}white black latino asian mv_thirties mv_fourties
{col 23}mv_fifties mv_sixties mv_seventies mv_income2quartile
{col 23}mv_income3quartile mv_income4quartile mv_incomenoanswer
{col 23}mv_female mv_college mv_christiannotcatholic mv_catholic
{col 23}mv_fulltimeworker mv_unemployed mv_selfemployed mv_outofLF
{col 23}mv_black mv_latino mv_asian mv_bluecollar mv_serviceworker
{col 23}4.country 7.country 9.country 11.country
{hline 78}
({res}est2{txt} stored)

{com}. 
. estimates store mod2
{txt}
{com}. estadd scalar fstat = e(widstat)

{txt}added scalar:
              e(fstat) =  {res}1.5008223
{txt}
{com}. estadd local CFE "X", replace

{txt}added macro:
                e(CFE) : "{res:X}"

{com}. estadd local Controls "X", replace

{txt}added macro:
           e(Controls) : "{res:X}"

{com}. estadd local IV "16 IVs"

{txt}added macro:
                 e(IV) : "{res:16 IVs}"

{com}. estadd local nothing nothing2 = " "

{txt}added macro:
            e(nothing) : "{res:nothing2 = " "}"

{com}. get_mean

{txt}added scalar:
                 e(av) =  {res}.493
{txt}
{com}. local coef2 = _b[satis_head]
{txt}
{com}. local se2 = _se[satis_head]
{txt}
{com}. 
. /* E.19. Col 3. satis_reg only */
. eststo: ivreg2 satis_dem ///
> (satis_reg = TH1_TE1 TH1_TE2 TH1_TE3 TH1_TE4 TH2_TE1 TH2_TE2 TH2_TE3 TH2_TE4 TH3_TE1 TH3_TE2 TH3_TE3 TH3_TE4 TH4_TE1 TH4_TE2 TH4_TE3), small robust
{res}
{txt}IV (2SLS) estimation
{hline 20}

Estimates efficient for homoskedasticity only
Statistics robust to heteroskedasticity

{col 55}Number of obs = {res}   19523
{txt}{col 55}F(  1, 19521) = {res}    4.44
{txt}{col 55}Prob > F      = {res}  0.0351
{txt}Total (centered) SS     = {res} 1412.790162{txt}{col 55}Centered R2   = {res}  0.0969
{txt}Total (uncentered) SS   = {res} 6156.910042{txt}{col 55}Uncentered R2 = {res}  0.7928
{txt}Residual SS             = {res} 1275.855626{txt}{col 55}Root MSE      = {res}   .2557

{txt}{hline 13}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 14}{c |}{col 26}    Robust
{col 1}   satis_dem{col 14}{c |} Coefficient{col 26}  std. err.{col 38}      t{col 46}   P>|t|{col 54}     [95% con{col 67}f. interval]
{hline 13}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 3}satis_reg {c |}{col 14}{res}{space 2} .6519794{col 26}{space 2} .3094452{col 37}{space 1}    2.11{col 46}{space 3}0.035{col 54}{space 4} .0454404{col 67}{space 3} 1.258519
{txt}{space 7}_cons {c |}{col 14}{res}{space 2} .1240094{col 26}{space 2} .1751482{col 37}{space 1}    0.71{col 46}{space 3}0.479{col 54}{space 4}-.2192961{col 67}{space 3} .4673149
{txt}{hline 13}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{help ivreg2##idtest:Underidentification test} (Kleibergen-Paap rk LM statistic):{res}{col 71}  11.186
{txt}{col 52}Chi-sq({res}15{txt}) P-val =  {res}{col 73}0.7393
{txt}{hline 78}
{help ivreg2##widtest:Weak identification test} (Cragg-Donald Wald F statistic):{res}{col 71}   0.752
{txt}                         (Kleibergen-Paap rk Wald F statistic):{res}{col 71}   0.746
{txt}Stock-Yogo weak ID test critical values:{res}{txt}{col 42} 5% maximal IV relative bias{res}{col 73} 21.23
{txt}{col 42}10% maximal IV relative bias{res}{col 73} 11.51
{txt}{col 42}20% maximal IV relative bias{res}{col 73}  6.42
{txt}{col 42}30% maximal IV relative bias{res}{col 73}  4.63
{txt}{col 42}10% maximal IV size{res}{col 73} 50.39
{txt}{col 42}15% maximal IV size{res}{col 73} 26.80
{txt}{col 42}20% maximal IV size{res}{col 73} 18.72
{txt}{col 42}25% maximal IV size{res}{col 73} 14.60
{txt}Source: Stock-Yogo (2005).  Reproduced by permission.
NB: Critical values are for Cragg-Donald F statistic and i.i.d. errors.
{hline 78}
{help ivreg2##overidtests:Hansen J statistic} (overidentification test of all instruments):{res}{col 71}   6.900
{txt}{col 52}Chi-sq({res}14{txt}) P-val =  {res}{col 73}0.9385
{txt}{hline 78}
Instrumented:{col 23}satis_reg
Excluded instruments:{col 23}TH1_TE1 TH1_TE2 TH1_TE3 TH1_TE4 TH2_TE1 TH2_TE2 TH2_TE3
{col 23}TH2_TE4 TH3_TE1 TH3_TE2 TH3_TE3 TH3_TE4 TH4_TE1 TH4_TE2
{col 23}TH4_TE3
{hline 78}
({res}est3{txt} stored)

{com}. 
. estimates store mod1r
{txt}
{com}. estadd scalar fstat = e(widstat)

{txt}added scalar:
              e(fstat) =  {res}.74613678
{txt}
{com}. estadd local IV "16 IVs"

{txt}added macro:
                 e(IV) : "{res:16 IVs}"

{com}. estadd local nothing nothing2 = " "

{txt}added macro:
            e(nothing) : "{res:nothing2 = " "}"

{com}. get_mean

{txt}added scalar:
                 e(av) =  {res}.493
{txt}
{com}. 
. /* E.19. Col 4. satis_reg only + controls*/
. eststo: ivreg2 satis_dem ///
> (satis_reg = TH1_TE1 TH1_TE2 TH1_TE3 TH1_TE4 TH2_TE1 TH2_TE2 TH2_TE3 TH2_TE4 TH3_TE1 TH3_TE2 TH3_TE3 TH3_TE4 TH4_TE1 TH4_TE2 TH4_TE3) $controls $mv_controls i.country, small robust
{txt}Warning - collinearities detected
Vars dropped:{col 21}white black latino asian mv_thirties mv_fourties mv_fifties
{col 21}mv_sixties mv_seventies mv_income2quartile
{col 21}mv_income3quartile mv_income4quartile mv_incomenoanswer
{col 21}mv_female mv_college mv_christiannotcatholic mv_catholic
{col 21}mv_fulltimeworker mv_unemployed mv_selfemployed mv_outofLF
{col 21}mv_black mv_latino mv_asian mv_bluecollar mv_serviceworker
{col 21}4.country 7.country 9.country 11.country
{res}
{txt}IV (2SLS) estimation
{hline 20}

Estimates efficient for homoskedasticity only
Statistics robust to heteroskedasticity

{col 55}Number of obs = {res}   19523
{txt}{col 55}F( 36, 19486) = {res} 2342.15
{txt}{col 55}Prob > F      = {res}  0.0000
{txt}Total (centered) SS     = {res} 1412.790162{txt}{col 55}Centered R2   = {res}  0.1426
{txt}Total (uncentered) SS   = {res} 6156.910042{txt}{col 55}Uncentered R2 = {res}  0.8033
{txt}Residual SS             = {res}   1211.3195{txt}{col 55}Root MSE      = {res}   .2493

{txt}{hline 24}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 25}{c |}{col 37}    Robust
{col 1}              satis_dem{col 25}{c |} Coefficient{col 37}  std. err.{col 49}      t{col 57}   P>|t|{col 65}     [95% con{col 78}f. interval]
{hline 24}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 14}satis_reg {c |}{col 25}{res}{space 2} .7060496{col 37}{space 2}  .290766{col 48}{space 1}    2.43{col 57}{space 3}0.015{col 65}{space 4} .1361233{col 78}{space 3} 1.275976
{txt}{space 15}thirties {c |}{col 25}{res}{space 2}-.0162392{col 37}{space 2} .0077245{col 48}{space 1}   -2.10{col 57}{space 3}0.036{col 65}{space 4}-.0313798{col 78}{space 3}-.0010985
{txt}{space 15}fourties {c |}{col 25}{res}{space 2}-.0138275{col 37}{space 2} .0100138{col 48}{space 1}   -1.38{col 57}{space 3}0.167{col 65}{space 4}-.0334554{col 78}{space 3} .0058004
{txt}{space 16}fifties {c |}{col 25}{res}{space 2}-.0166833{col 37}{space 2} .0089471{col 48}{space 1}   -1.86{col 57}{space 3}0.062{col 65}{space 4}-.0342204{col 78}{space 3} .0008538
{txt}{space 16}sixties {c |}{col 25}{res}{space 2}-.0097336{col 37}{space 2} .0071697{col 48}{space 1}   -1.36{col 57}{space 3}0.175{col 65}{space 4}-.0237868{col 78}{space 3} .0043197
{txt}{space 14}seventies {c |}{col 25}{res}{space 2} .0077884{col 37}{space 2}  .008109{col 48}{space 1}    0.96{col 57}{space 3}0.337{col 65}{space 4}-.0081058{col 78}{space 3} .0236827
{txt}{space 8}income2quartile {c |}{col 25}{res}{space 2} .0122138{col 37}{space 2} .0084029{col 48}{space 1}    1.45{col 57}{space 3}0.146{col 65}{space 4}-.0042566{col 78}{space 3} .0286842
{txt}{space 8}income3quartile {c |}{col 25}{res}{space 2}   .02463{col 37}{space 2} .0078549{col 48}{space 1}    3.14{col 57}{space 3}0.002{col 65}{space 4} .0092337{col 78}{space 3} .0400264
{txt}{space 8}income4quartile {c |}{col 25}{res}{space 2} .0524853{col 37}{space 2}  .007053{col 48}{space 1}    7.44{col 57}{space 3}0.000{col 65}{space 4} .0386609{col 78}{space 3} .0663098
{txt}{space 9}incomenoanswer {c |}{col 25}{res}{space 2} .0154867{col 37}{space 2} .0097463{col 48}{space 1}    1.59{col 57}{space 3}0.112{col 65}{space 4}-.0036169{col 78}{space 3} .0345903
{txt}{space 17}female {c |}{col 25}{res}{space 2}-.0089754{col 37}{space 2} .0063206{col 48}{space 1}   -1.42{col 57}{space 3}0.156{col 65}{space 4}-.0213643{col 78}{space 3} .0034135
{txt}{space 13}highschool {c |}{col 25}{res}{space 2} .0175834{col 37}{space 2} .0068134{col 48}{space 1}    2.58{col 57}{space 3}0.010{col 65}{space 4} .0042285{col 78}{space 3} .0309383
{txt}{space 16}college {c |}{col 25}{res}{space 2} .0319034{col 37}{space 2} .0060408{col 48}{space 1}    5.28{col 57}{space 3}0.000{col 65}{space 4} .0200629{col 78}{space 3} .0437438
{txt}{space 13}noreligion {c |}{col 25}{res}{space 2}-.0233259{col 37}{space 2} .0088421{col 48}{space 1}   -2.64{col 57}{space 3}0.008{col 65}{space 4}-.0406571{col 78}{space 3}-.0059946
{txt}{space 3}christiannotcatholic {c |}{col 25}{res}{space 2} .0234513{col 37}{space 2} .0161585{col 48}{space 1}    1.45{col 57}{space 3}0.147{col 65}{space 4}-.0082207{col 78}{space 3} .0551234
{txt}{space 15}catholic {c |}{col 25}{res}{space 2} .0045811{col 37}{space 2} .0155671{col 48}{space 1}    0.29{col 57}{space 3}0.769{col 65}{space 4}-.0259317{col 78}{space 3} .0350939
{txt}{space 9}fulltimeworker {c |}{col 25}{res}{space 2}-.1436164{col 37}{space 2} .0234565{col 48}{space 1}   -6.12{col 57}{space 3}0.000{col 65}{space 4}-.1895932{col 78}{space 3}-.0976397
{txt}{space 9}parttimeworker {c |}{col 25}{res}{space 2}-.1080405{col 37}{space 2} .0349671{col 48}{space 1}   -3.09{col 57}{space 3}0.002{col 65}{space 4}-.1765791{col 78}{space 3} -.039502
{txt}{space 13}unemployed {c |}{col 25}{res}{space 2}-.1637792{col 37}{space 2} .0289005{col 48}{space 1}   -5.67{col 57}{space 3}0.000{col 65}{space 4}-.2204266{col 78}{space 3}-.1071317
{txt}{space 11}selfemployed {c |}{col 25}{res}{space 2}-.1794497{col 37}{space 2} .0395281{col 48}{space 1}   -4.54{col 57}{space 3}0.000{col 65}{space 4}-.2569282{col 78}{space 3}-.1019712
{txt}{space 16}outofLF {c |}{col 25}{res}{space 2}-.1569922{col 37}{space 2} .0220505{col 48}{space 1}   -7.12{col 57}{space 3}0.000{col 65}{space 4}-.2002131{col 78}{space 3}-.1137713
{txt}{space 13}goodhealth {c |}{col 25}{res}{space 2} .0172324{col 37}{space 2} .0140338{col 48}{space 1}    1.23{col 57}{space 3}0.219{col 65}{space 4} -.010275{col 78}{space 3} .0447397
{txt}{space 18}white {c |}{col 25}{res}{space 2}        0{col 37}{txt}  (omitted)
{space 18}black {c |}{col 25}{res}{space 2}        0{col 37}{txt}  (omitted)
{space 17}latino {c |}{col 25}{res}{space 2}        0{col 37}{txt}  (omitted)
{space 18}asian {c |}{col 25}{res}{space 2}        0{col 37}{txt}  (omitted)
{space 12}whitecollar {c |}{col 25}{res}{space 2}-.0066425{col 37}{space 2} .0087426{col 48}{space 1}   -0.76{col 57}{space 3}0.447{col 65}{space 4}-.0237786{col 78}{space 3} .0104937
{txt}{space 13}bluecollar {c |}{col 25}{res}{space 2}-.0103722{col 37}{space 2} .0097721{col 48}{space 1}   -1.06{col 57}{space 3}0.289{col 65}{space 4}-.0295263{col 78}{space 3} .0087819
{txt}{space 10}serviceworker {c |}{col 25}{res}{space 2}-.0136613{col 37}{space 2} .0069619{col 48}{space 1}   -1.96{col 57}{space 3}0.050{col 65}{space 4}-.0273073{col 78}{space 3}-.0000154
{txt}{space 12}mv_thirties {c |}{col 25}{res}{space 2}        0{col 37}{txt}  (omitted)
{space 12}mv_fourties {c |}{col 25}{res}{space 2}        0{col 37}{txt}  (omitted)
{space 13}mv_fifties {c |}{col 25}{res}{space 2}        0{col 37}{txt}  (omitted)
{space 13}mv_sixties {c |}{col 25}{res}{space 2}        0{col 37}{txt}  (omitted)
{space 11}mv_seventies {c |}{col 25}{res}{space 2}        0{col 37}{txt}  (omitted)
{space 5}mv_income2quartile {c |}{col 25}{res}{space 2}        0{col 37}{txt}  (omitted)
{space 5}mv_income3quartile {c |}{col 25}{res}{space 2}        0{col 37}{txt}  (omitted)
{space 5}mv_income4quartile {c |}{col 25}{res}{space 2}        0{col 37}{txt}  (omitted)
{space 6}mv_incomenoanswer {c |}{col 25}{res}{space 2}        0{col 37}{txt}  (omitted)
{space 14}mv_female {c |}{col 25}{res}{space 2}        0{col 37}{txt}  (omitted)
{space 10}mv_highschool {c |}{col 25}{res}{space 2} .0118655{col 37}{space 2} .0142599{col 48}{space 1}    0.83{col 57}{space 3}0.405{col 65}{space 4}-.0160852{col 78}{space 3} .0398162
{txt}{space 13}mv_college {c |}{col 25}{res}{space 2}        0{col 37}{txt}  (omitted)
{space 10}mv_noreligion {c |}{col 25}{res}{space 2}-.0213706{col 37}{space 2} .0326098{col 48}{space 1}   -0.66{col 57}{space 3}0.512{col 65}{space 4}-.0852885{col 78}{space 3} .0425474
{txt}mv_christiannotcatholic {c |}{col 25}{res}{space 2}        0{col 37}{txt}  (omitted)
{space 12}mv_catholic {c |}{col 25}{res}{space 2}        0{col 37}{txt}  (omitted)
{space 6}mv_fulltimeworker {c |}{col 25}{res}{space 2}        0{col 37}{txt}  (omitted)
{space 6}mv_parttimeworker {c |}{col 25}{res}{space 2}-.0607473{col 37}{space 2} .0344159{col 48}{space 1}   -1.77{col 57}{space 3}0.078{col 65}{space 4}-.1282054{col 78}{space 3} .0067108
{txt}{space 10}mv_unemployed {c |}{col 25}{res}{space 2}        0{col 37}{txt}  (omitted)
{space 8}mv_selfemployed {c |}{col 25}{res}{space 2}        0{col 37}{txt}  (omitted)
{space 13}mv_outofLF {c |}{col 25}{res}{space 2}        0{col 37}{txt}  (omitted)
{space 10}mv_goodhealth {c |}{col 25}{res}{space 2} .4217085{col 37}{space 2} .0711394{col 48}{space 1}    5.93{col 57}{space 3}0.000{col 65}{space 4} .2822692{col 78}{space 3} .5611479
{txt}{space 15}mv_white {c |}{col 25}{res}{space 2}-.0184194{col 37}{space 2} .0308583{col 48}{space 1}   -0.60{col 57}{space 3}0.551{col 65}{space 4}-.0789042{col 78}{space 3} .0420655
{txt}{space 15}mv_black {c |}{col 25}{res}{space 2}        0{col 37}{txt}  (omitted)
{space 14}mv_latino {c |}{col 25}{res}{space 2}        0{col 37}{txt}  (omitted)
{space 15}mv_asian {c |}{col 25}{res}{space 2}        0{col 37}{txt}  (omitted)
{space 9}mv_whitecollar {c |}{col 25}{res}{space 2} .0529097{col 37}{space 2} .0387496{col 48}{space 1}    1.37{col 57}{space 3}0.172{col 65}{space 4} -.023043{col 78}{space 3} .1288623
{txt}{space 10}mv_bluecollar {c |}{col 25}{res}{space 2}        0{col 37}{txt}  (omitted)
{space 7}mv_serviceworker {c |}{col 25}{res}{space 2}        0{col 37}{txt}  (omitted)
{space 23} {c |}
{space 16}country {c |}
{space 16}Brazil  {c |}{col 25}{res}{space 2} -.019036{col 37}{space 2} .0315312{col 48}{space 1}   -0.60{col 57}{space 3}0.546{col 65}{space 4}-.0808399{col 78}{space 3} .0427679
{txt}{space 16}France  {c |}{col 25}{res}{space 2}        0{col 37}{txt}  (omitted)
{space 15}Germany  {c |}{col 25}{res}{space 2} .0070681{col 37}{space 2} .0089863{col 48}{space 1}    0.79{col 57}{space 3}0.432{col 65}{space 4}-.0105458{col 78}{space 3} .0246819
{txt}{space 17}Italy  {c |}{col 25}{res}{space 2} -.036346{col 37}{space 2} .0478692{col 48}{space 1}   -0.76{col 57}{space 3}0.448{col 65}{space 4}-.1301737{col 78}{space 3} .0574818
{txt}{space 11}New_Zealand  {c |}{col 25}{res}{space 2}        0{col 37}{txt}  (omitted)
{space 16}Poland  {c |}{col 25}{res}{space 2}-.1046819{col 37}{space 2}  .018599{col 48}{space 1}   -5.63{col 57}{space 3}0.000{col 65}{space 4}-.1411376{col 78}{space 3}-.0682262
{txt}{space 17}Spain  {c |}{col 25}{res}{space 2}        0{col 37}{txt}  (omitted)
{space 16}Sweden  {c |}{col 25}{res}{space 2} .1101615{col 37}{space 2} .0189327{col 48}{space 1}    5.82{col 57}{space 3}0.000{col 65}{space 4} .0730519{col 78}{space 3} .1472711
{txt}{space 20}UK  {c |}{col 25}{res}{space 2}        0{col 37}{txt}  (omitted)
{space 23} {c |}
{space 18}_cons {c |}{col 25}{res}{space 2} .1841007{col 37}{space 2} .1328396{col 48}{space 1}    1.39{col 57}{space 3}0.166{col 65}{space 4}-.0762763{col 78}{space 3} .4444776
{txt}{hline 24}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{help ivreg2##idtest:Underidentification test} (Kleibergen-Paap rk LM statistic):{res}{col 71}  12.750
{txt}{col 52}Chi-sq({res}15{txt}) P-val =  {res}{col 73}0.6216
{txt}{hline 78}
{help ivreg2##widtest:Weak identification test} (Cragg-Donald Wald F statistic):{res}{col 71}   0.848
{txt}                         (Kleibergen-Paap rk Wald F statistic):{res}{col 71}   0.849
{txt}Stock-Yogo weak ID test critical values:{res}{txt}{col 42} 5% maximal IV relative bias{res}{col 73} 21.23
{txt}{col 42}10% maximal IV relative bias{res}{col 73} 11.51
{txt}{col 42}20% maximal IV relative bias{res}{col 73}  6.42
{txt}{col 42}30% maximal IV relative bias{res}{col 73}  4.63
{txt}{col 42}10% maximal IV size{res}{col 73} 50.39
{txt}{col 42}15% maximal IV size{res}{col 73} 26.80
{txt}{col 42}20% maximal IV size{res}{col 73} 18.72
{txt}{col 42}25% maximal IV size{res}{col 73} 14.60
{txt}Source: Stock-Yogo (2005).  Reproduced by permission.
NB: Critical values are for Cragg-Donald F statistic and i.i.d. errors.
{hline 78}
{err}Warning: estimated covariance matrix of moment conditions not of full rank.
         overidentification statistic not reported, and standard errors and
         model tests should be interpreted with caution.
Possible causes:
         singleton dummy variable (dummy with one 1 and N-1 0s or vice versa)
{help ivreg2##partial:partial} option may address problem.
{txt}{hline 78}
Instrumented:{col 23}satis_reg
Included instruments:{col 23}thirties fourties fifties sixties seventies
{col 23}income2quartile income3quartile income4quartile
{col 23}incomenoanswer female highschool college noreligion
{col 23}christiannotcatholic catholic fulltimeworker
{col 23}parttimeworker unemployed selfemployed outofLF goodhealth
{col 23}whitecollar bluecollar serviceworker mv_highschool
{col 23}mv_noreligion mv_parttimeworker mv_goodhealth mv_white
{col 23}mv_whitecollar 3.country 5.country 6.country 8.country
{col 23}10.country
Excluded instruments:{col 23}TH1_TE1 TH1_TE2 TH1_TE3 TH1_TE4 TH2_TE1 TH2_TE2 TH2_TE3
{col 23}TH2_TE4 TH3_TE1 TH3_TE2 TH3_TE3 TH3_TE4 TH4_TE1 TH4_TE2
{col 23}TH4_TE3
Dropped collinear:{col 23}white black latino asian mv_thirties mv_fourties
{col 23}mv_fifties mv_sixties mv_seventies mv_income2quartile
{col 23}mv_income3quartile mv_income4quartile mv_incomenoanswer
{col 23}mv_female mv_college mv_christiannotcatholic mv_catholic
{col 23}mv_fulltimeworker mv_unemployed mv_selfemployed mv_outofLF
{col 23}mv_black mv_latino mv_asian mv_bluecollar mv_serviceworker
{col 23}4.country 7.country 9.country 11.country
{hline 78}
({res}est4{txt} stored)

{com}. 
. estimates store mod2r
{txt}
{com}. estadd scalar fstat = e(widstat)

{txt}added scalar:
              e(fstat) =  {res}.8487989
{txt}
{com}. estadd local CFE "X", replace

{txt}added macro:
                e(CFE) : "{res:X}"

{com}. estadd local Controls "X", replace

{txt}added macro:
           e(Controls) : "{res:X}"

{com}. estadd local IV "16 IVs"

{txt}added macro:
                 e(IV) : "{res:16 IVs}"

{com}. estadd local nothing nothing2 = " "

{txt}added macro:
            e(nothing) : "{res:nothing2 = " "}"

{com}. get_mean

{txt}added scalar:
                 e(av) =  {res}.493
{txt}
{com}. 
. /* E.19. Col 5. satis_head + satis_reg */
. * Hausman test for following regression
. qui ivreg2 satis_dem ///
> (satis_head satis_reg = TH1_TE1 TH1_TE2 TH1_TE3 TH1_TE4 TH2_TE1 TH2_TE2 TH2_TE3 TH2_TE4 TH3_TE1 TH3_TE2 TH3_TE3 TH3_TE4 TH4_TE1 TH4_TE2 TH4_TE3), small 
{txt}
{com}. estimates store mod3b
{txt}
{com}. hausman mod3b mod1b

{txt}{col 18}{hline 4} Coefficients {hline 4}
{col 14}{c |}{col 21}(b){col 34}(B){col 49}(b-B){col 59}sqrt(diag(V_b-V_B))
{col 14}{c |}{col 17}   mod3b    {col 30}   mod1b    {col 46}Difference{col 63}Std. err.
{hline 13}{c +}{hline 64}
{space 2}satis_head {c |}{res}{col 18} .4214186{col 31} .4968142{col 47}-.0753956{col 63} .1600934
{txt}{hline 13}{c BT}{hline 64}
{ralign 78:b = Consistent under H0 and Ha; obtained from {bf:ivreg2}.}
{ralign 78:B = Inconsistent under Ha, efficient under H0; obtained from {bf:ivreg2}.}

Test of H0: Difference in coefficients not systematic

{ralign 11:chi2({res:1})} = (b-B)'[(V_b-V_B)^(-1)](b-B)
{ralign 11:} = {res:{ralign 6:0.22}}
{ralign 11:Prob > chi2} = {res:{ralign 6:0.6377}}

{com}. local temp_hausman = r(p)
{txt}
{com}. 
. eststo: ivreg2 satis_dem ///
> (satis_head satis_reg = TH1_TE1 TH1_TE2 TH1_TE3 TH1_TE4 TH2_TE1 TH2_TE2 TH2_TE3 TH2_TE4 TH3_TE1 TH3_TE2 TH3_TE3 TH3_TE4 TH4_TE1 TH4_TE2 TH4_TE3), small robust
{res}
{txt}IV (2SLS) estimation
{hline 20}

Estimates efficient for homoskedasticity only
Statistics robust to heteroskedasticity

{col 55}Number of obs = {res}   19523
{txt}{col 55}F(  2, 19520) = {res}    5.36
{txt}{col 55}Prob > F      = {res}  0.0047
{txt}Total (centered) SS     = {res} 1412.790162{txt}{col 55}Centered R2   = {res}  0.4323
{txt}Total (uncentered) SS   = {res} 6156.910042{txt}{col 55}Uncentered R2 = {res}  0.8697
{txt}Residual SS             = {res} 802.0277399{txt}{col 55}Root MSE      = {res}   .2027

{txt}{hline 13}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 14}{c |}{col 26}    Robust
{col 1}   satis_dem{col 14}{c |} Coefficient{col 26}  std. err.{col 38}      t{col 46}   P>|t|{col 54}     [95% con{col 67}f. interval]
{hline 13}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 2}satis_head {c |}{col 14}{res}{space 2} .4214186{col 26}{space 2} .2258209{col 37}{space 1}    1.87{col 46}{space 3}0.062{col 54}{space 4}-.0212098{col 67}{space 3} .8640469
{txt}{space 3}satis_reg {c |}{col 14}{res}{space 2} .1660084{col 26}{space 2} .3580913{col 37}{space 1}    0.46{col 46}{space 3}0.643{col 54}{space 4}-.5358811{col 67}{space 3} .8678979
{txt}{space 7}_cons {c |}{col 14}{res}{space 2} .2075994{col 26}{space 2} .1453616{col 37}{space 1}    1.43{col 46}{space 3}0.153{col 54}{space 4}-.0773219{col 67}{space 3} .4925206
{txt}{hline 13}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{help ivreg2##idtest:Underidentification test} (Kleibergen-Paap rk LM statistic):{res}{col 71}   5.941
{txt}{col 52}Chi-sq({res}14{txt}) P-val =  {res}{col 73}0.9680
{txt}{hline 78}
{help ivreg2##widtest:Weak identification test} (Cragg-Donald Wald F statistic):{res}{col 71}   0.399
{txt}                         (Kleibergen-Paap rk Wald F statistic):{res}{col 71}   0.396
{txt}Stock-Yogo weak ID test critical values:{res}{txt}{col 42} 5% maximal IV relative bias{res}{col 73} 19.98
{txt}{col 42}10% maximal IV relative bias{res}{col 73} 10.93
{txt}{col 42}20% maximal IV relative bias{res}{col 73}  6.19
{txt}{col 42}30% maximal IV relative bias{res}{col 73}  4.50
{txt}{col 42}10% maximal IV size{res}{col 73} 38.08
{txt}{col 42}15% maximal IV size{res}{col 73} 20.60
{txt}{col 42}20% maximal IV size{res}{col 73} 14.65
{txt}{col 42}25% maximal IV size{res}{col 73} 11.58
{txt}Source: Stock-Yogo (2005).  Reproduced by permission.
NB: Critical values are for Cragg-Donald F statistic and i.i.d. errors.
{hline 78}
{help ivreg2##overidtests:Hansen J statistic} (overidentification test of all instruments):{res}{col 71}   7.484
{txt}{col 52}Chi-sq({res}13{txt}) P-val =  {res}{col 73}0.8755
{txt}{hline 78}
Instrumented:{col 23}satis_head satis_reg
Excluded instruments:{col 23}TH1_TE1 TH1_TE2 TH1_TE3 TH1_TE4 TH2_TE1 TH2_TE2 TH2_TE3
{col 23}TH2_TE4 TH3_TE1 TH3_TE2 TH3_TE3 TH3_TE4 TH4_TE1 TH4_TE2
{col 23}TH4_TE3
{hline 78}
({res}est5{txt} stored)

{com}.  
. estimates store mod3
{txt}
{com}. estadd scalar fstat = e(widstat)

{txt}added scalar:
              e(fstat) =  {res}.39612273
{txt}
{com}. estadd local IV "16 IVs"

{txt}added macro:
                 e(IV) : "{res:16 IVs}"

{com}. estadd local nothing nothing2 = " "

{txt}added macro:
            e(nothing) : "{res:nothing2 = " "}"

{com}. get_mean

{txt}added scalar:
                 e(av) =  {res}.493
{txt}
{com}. estadd scalar hausman = `temp_hausman'

{txt}added scalar:
            e(hausman) =  {res}.63767815
{txt}
{com}. local coef3 = _b[satis_head]
{txt}
{com}. local se3 = _se[satis_head]
{txt}
{com}. 
. * Z-test 
. local z_value = abs(`coef3'-`coef1')/sqrt(`se3'^2+`se1'^2)
{txt}
{com}. di (1-normal(`z_value'))*2
{res}.78339731
{txt}
{com}. estadd scalar wald = (1-normal(`z_value'))*2

{txt}added scalar:
               e(wald) =  {res}.78339731
{txt}
{com}. 
. /* E.19. Col 6. satis_head + satis_reg + controls*/
. * Hausman test for following regression
. qui ivreg2 satis_dem ///
> (satis_head satis_reg = TH1_TE1 TH1_TE2 TH1_TE3 TH1_TE4 TH2_TE1 TH2_TE2 TH2_TE3 TH2_TE4 TH3_TE1 TH3_TE2 TH3_TE3 TH3_TE4 TH4_TE1 TH4_TE2 TH4_TE3) $controls $mv_controls i.country, small 
{txt}
{com}. estimates store mod4b
{txt}
{com}. hausman mod4b mod2b

{txt}{col 18}{hline 4} Coefficients {hline 4}
{col 14}{c |}{col 21}(b){col 34}(B){col 49}(b-B){col 59}sqrt(diag(V_b-V_B))
{col 14}{c |}{col 17}   mod4b    {col 30}   mod2b    {col 46}Difference{col 63}Std. err.
{hline 13}{c +}{hline 64}
{space 2}satis_head {c |}{res}{col 18} .4085132{col 31} .5122516{col 47}-.1037384{col 63} .1575185
{txt}{space 4}thirties {c |}{res}{col 18}-.0162613{col 31}-.0178843{col 47}  .001623{col 63} .0023157
{txt}{space 4}fourties {c |}{res}{col 18}-.0116899{col 31}-.0139635{col 47} .0022736{col 63} .0032152
{txt}{space 5}fifties {c |}{res}{col 18}-.0121296{col 31}-.0131575{col 47} .0010279{col 63} .0011375
{txt}{space 5}sixties {c |}{res}{col 18}-.0011005{col 31} .0000869{col 47}-.0011874{col 63} .0015659
{txt}{space 3}seventies {c |}{res}{col 18} .0137746{col 31}  .015896{col 47}-.0021214{col 63} .0030395
{txt}income2qua~e {c |}{res}{col 18} .0141342{col 31}  .016901{col 47}-.0027668{col 63} .0041464
{txt}income3qua~e {c |}{res}{col 18} .0226692{col 31} .0241417{col 47}-.0014725{col 63} .0020759
{txt}income4qua~e {c |}{res}{col 18} .0406782{col 31} .0391394{col 47} .0015388{col 63} .0021117
{txt}incomenoan~r {c |}{res}{col 18} .0092337{col 31} .0055897{col 47} .0036441{col 63} .0054128
{txt}{space 6}female {c |}{res}{col 18}-.0084204{col 31}-.0064257{col 47}-.0019947{col 63} .0029218
{txt}{space 2}highschool {c |}{res}{col 18} .0094235{col 31} .0063657{col 47} .0030578{col 63} .0046099
{txt}{space 5}college {c |}{res}{col 18} .0259069{col 31} .0238042{col 47} .0021027{col 63}   .00315
{txt}{space 2}noreligion {c |}{res}{col 18}-.0063789{col 31}-.0009456{col 47}-.0054334{col 63} .0082029
{txt}christiann~c {c |}{res}{col 18} .0226808{col 31} .0271262{col 47}-.0044454{col 63} .0065217
{txt}{space 4}catholic {c |}{res}{col 18} .0197837{col 31} .0283307{col 47} -.008547{col 63}  .012926
{txt}fulltimewo~r {c |}{res}{col 18}-.0533633{col 31}-.0382945{col 47}-.0150688{col 63}  .022374
{txt}parttimewo~r {c |}{res}{col 18}-.0181172{col 31}-.0032072{col 47}  -.01491{col 63} .0220013
{txt}{space 2}unemployed {c |}{res}{col 18}-.0655001{col 31}-.0496231{col 47} -.015877{col 63}  .023482
{txt}selfemployed {c |}{res}{col 18}-.0530025{col 31}-.0260848{col 47}-.0269177{col 63} .0405351
{txt}{space 5}outofLF {c |}{res}{col 18}-.0561813{col 31}-.0377784{col 47}-.0184029{col 63} .0275674
{txt}{space 2}goodhealth {c |}{res}{col 18} .0198142{col 31} .0252403{col 47}-.0054262{col 63} .0081281
{txt}{space 1}whitecollar {c |}{res}{col 18}  .001069{col 31} .0032383{col 47}-.0021693{col 63} .0032012
{txt}{space 2}bluecollar {c |}{res}{col 18}-.0092155{col 31} -.010353{col 47} .0011376{col 63} .0013404
{txt}servicewor~r {c |}{res}{col 18}-.0105732{col 31}-.0098787{col 47}-.0006945{col 63} .0007631
{txt}mv_highsch~l {c |}{res}{col 18} .0210343{col 31} .0246177{col 47}-.0035834{col 63} .0052354
{txt}mv_norelig~n {c |}{res}{col 18}  .000241{col 31} .0106878{col 47}-.0104469{col 63} .0156295
{txt}mv_parttim~r {c |}{res}{col 18}-.0085311{col 31} .0164377{col 47}-.0249688{col 63} .0379147
{txt}mv_goodhea~h {c |}{res}{col 18} .4903857{col 31} .5326308{col 47}-.0422452{col 63} .0583564
{txt}{space 4}mv_white {c |}{res}{col 18}-.0060892{col 31} -.012144{col 47} .0060548{col 63} .0083447
{txt}mv_whiteco~r {c |}{res}{col 18}-.0137181{col 31}-.0183543{col 47} .0046362{col 63} .0034938
{txt}{space 5}country {c |}
{space 10}3  {c |}{res}{col 18}-.0557551{col 31} -.074184{col 47} .0184289{col 63} .0279279
{txt}{space 10}5  {c |}{res}{col 18}-.0147899{col 31}-.0215543{col 47} .0067645{col 63} .0102357
{txt}{space 10}6  {c |}{res}{col 18}-.0864972{col 31} -.112316{col 47} .0258187{col 63} .0390556
{txt}{space 10}8  {c |}{res}{col 18}-.0976516{col 31}-.0972162{col 47}-.0004354{col 63}        .
{txt}{space 9}10  {c |}{res}{col 18} .0760169{col 31} .0648198{col 47} .0111971{col 63} .0170791
{txt}{hline 13}{c BT}{hline 64}
{ralign 78:b = Consistent under H0 and Ha; obtained from {bf:ivreg2}.}
{ralign 78:B = Inconsistent under Ha, efficient under H0; obtained from {bf:ivreg2}.}

Test of H0: Difference in coefficients not systematic

{ralign 11:chi2({res:36})} = (b-B)'[(V_b-V_B)^(-1)](b-B)
{ralign 11:} = {res:{ralign 6:0.40}}
{ralign 11:Prob > chi2} = {res:{ralign 6:1.0000}}
(V_b-V_B is not positive definite)

{com}. local temp_hausman = r(p)
{txt}
{com}. 
. * to get the Cragg-Donald statistic: 
. eststo: ivreg2 satis_dem ///
> (satis_head satis_reg = TH1_TE1 TH1_TE2 TH1_TE3 TH1_TE4 TH2_TE1 TH2_TE2 TH2_TE3 TH2_TE4 TH3_TE1 TH3_TE2 TH3_TE3 TH3_TE4 TH4_TE1 TH4_TE2 TH4_TE3) $controls $mv_controls i.country, small robust 
{txt}Warning - collinearities detected
Vars dropped:{col 21}white black latino asian mv_thirties mv_fourties mv_fifties
{col 21}mv_sixties mv_seventies mv_income2quartile
{col 21}mv_income3quartile mv_income4quartile mv_incomenoanswer
{col 21}mv_female mv_college mv_christiannotcatholic mv_catholic
{col 21}mv_fulltimeworker mv_unemployed mv_selfemployed mv_outofLF
{col 21}mv_black mv_latino mv_asian mv_bluecollar mv_serviceworker
{col 21}4.country 7.country 9.country 11.country
{res}
{txt}IV (2SLS) estimation
{hline 20}

Estimates efficient for homoskedasticity only
Statistics robust to heteroskedasticity

{col 55}Number of obs = {res}   19523
{txt}{col 55}F( 37, 19485) = {res} 3751.40
{txt}{col 55}Prob > F      = {res}  0.0000
{txt}Total (centered) SS     = {res} 1412.790162{txt}{col 55}Centered R2   = {res}  0.4641
{txt}Total (uncentered) SS   = {res} 6156.910042{txt}{col 55}Uncentered R2 = {res}  0.8770
{txt}Residual SS             = {res} 757.0538431{txt}{col 55}Root MSE      = {res}   .1971

{txt}{hline 24}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 25}{c |}{col 37}    Robust
{col 1}              satis_dem{col 25}{c |} Coefficient{col 37}  std. err.{col 49}      t{col 57}   P>|t|{col 65}     [95% con{col 78}f. interval]
{hline 24}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 13}satis_head {c |}{col 25}{res}{space 2} .4085132{col 37}{space 2}  .217853{col 48}{space 1}    1.88{col 57}{space 3}0.061{col 65}{space 4}-.0184974{col 78}{space 3} .8355238
{txt}{space 14}satis_reg {c |}{col 25}{res}{space 2} .2253219{col 37}{space 2} .3461447{col 48}{space 1}    0.65{col 57}{space 3}0.515{col 65}{space 4}-.4531514{col 78}{space 3} .9037953
{txt}{space 15}thirties {c |}{col 25}{res}{space 2}-.0162613{col 37}{space 2} .0061333{col 48}{space 1}   -2.65{col 57}{space 3}0.008{col 65}{space 4}-.0282831{col 78}{space 3}-.0042395
{txt}{space 15}fourties {c |}{col 25}{res}{space 2}-.0116899{col 37}{space 2} .0079962{col 48}{space 1}   -1.46{col 57}{space 3}0.144{col 65}{space 4}-.0273631{col 78}{space 3} .0039834
{txt}{space 16}fifties {c |}{col 25}{res}{space 2}-.0121296{col 37}{space 2}  .007509{col 48}{space 1}   -1.62{col 57}{space 3}0.106{col 65}{space 4}-.0268478{col 78}{space 3} .0025886
{txt}{space 16}sixties {c |}{col 25}{res}{space 2}-.0011005{col 37}{space 2} .0072559{col 48}{space 1}   -0.15{col 57}{space 3}0.879{col 65}{space 4}-.0153226{col 78}{space 3} .0131216
{txt}{space 14}seventies {c |}{col 25}{res}{space 2} .0137746{col 37}{space 2} .0070822{col 48}{space 1}    1.94{col 57}{space 3}0.052{col 65}{space 4}-.0001072{col 78}{space 3} .0276564
{txt}{space 8}income2quartile {c |}{col 25}{res}{space 2} .0141342{col 37}{space 2} .0067093{col 48}{space 1}    2.11{col 57}{space 3}0.035{col 65}{space 4} .0009833{col 78}{space 3} .0272851
{txt}{space 8}income3quartile {c |}{col 25}{res}{space 2} .0226692{col 37}{space 2} .0062622{col 48}{space 1}    3.62{col 57}{space 3}0.000{col 65}{space 4} .0103948{col 78}{space 3} .0349436
{txt}{space 8}income4quartile {c |}{col 25}{res}{space 2} .0406782{col 37}{space 2} .0083708{col 48}{space 1}    4.86{col 57}{space 3}0.000{col 65}{space 4} .0242707{col 78}{space 3} .0570857
{txt}{space 9}incomenoanswer {c |}{col 25}{res}{space 2} .0092337{col 37}{space 2} .0085764{col 48}{space 1}    1.08{col 57}{space 3}0.282{col 65}{space 4}-.0075768{col 78}{space 3} .0260442
{txt}{space 17}female {c |}{col 25}{res}{space 2}-.0084204{col 37}{space 2} .0050091{col 48}{space 1}   -1.68{col 57}{space 3}0.093{col 65}{space 4}-.0182387{col 78}{space 3} .0013979
{txt}{space 13}highschool {c |}{col 25}{res}{space 2} .0094235{col 37}{space 2} .0069106{col 48}{space 1}    1.36{col 57}{space 3}0.173{col 65}{space 4}-.0041218{col 78}{space 3} .0229688
{txt}{space 16}college {c |}{col 25}{res}{space 2} .0259069{col 37}{space 2}  .005667{col 48}{space 1}    4.57{col 57}{space 3}0.000{col 65}{space 4} .0147992{col 78}{space 3} .0370147
{txt}{space 13}noreligion {c |}{col 25}{res}{space 2}-.0063789{col 37}{space 2} .0116714{col 48}{space 1}   -0.55{col 57}{space 3}0.585{col 65}{space 4}-.0292558{col 78}{space 3} .0164979
{txt}{space 3}christiannotcatholic {c |}{col 25}{res}{space 2} .0226808{col 37}{space 2} .0129574{col 48}{space 1}    1.75{col 57}{space 3}0.080{col 65}{space 4}-.0027169{col 78}{space 3} .0480784
{txt}{space 15}catholic {c |}{col 25}{res}{space 2} .0197837{col 37}{space 2} .0149298{col 48}{space 1}    1.33{col 57}{space 3}0.185{col 65}{space 4}-.0094801{col 78}{space 3} .0490474
{txt}{space 9}fulltimeworker {c |}{col 25}{res}{space 2}-.0533633{col 37}{space 2} .0514455{col 48}{space 1}   -1.04{col 57}{space 3}0.300{col 65}{space 4}-.1542009{col 78}{space 3} .0474743
{txt}{space 9}parttimeworker {c |}{col 25}{res}{space 2}-.0181172{col 37}{space 2} .0557569{col 48}{space 1}   -0.32{col 57}{space 3}0.745{col 65}{space 4}-.1274055{col 78}{space 3} .0911712
{txt}{space 13}unemployed {c |}{col 25}{res}{space 2}-.0655001{col 37}{space 2} .0569183{col 48}{space 1}   -1.15{col 57}{space 3}0.250{col 65}{space 4}-.1770648{col 78}{space 3} .0460645
{txt}{space 11}selfemployed {c |}{col 25}{res}{space 2}-.0530025{col 37}{space 2} .0749348{col 48}{space 1}   -0.71{col 57}{space 3}0.479{col 65}{space 4}-.1998811{col 78}{space 3} .0938761
{txt}{space 16}outofLF {c |}{col 25}{res}{space 2}-.0561813{col 37}{space 2} .0563719{col 48}{space 1}   -1.00{col 57}{space 3}0.319{col 65}{space 4} -.166675{col 78}{space 3} .0543125
{txt}{space 13}goodhealth {c |}{col 25}{res}{space 2} .0198142{col 37}{space 2} .0111649{col 48}{space 1}    1.77{col 57}{space 3}0.076{col 65}{space 4}-.0020701{col 78}{space 3} .0416984
{txt}{space 18}white {c |}{col 25}{res}{space 2}        0{col 37}{txt}  (omitted)
{space 18}black {c |}{col 25}{res}{space 2}        0{col 37}{txt}  (omitted)
{space 17}latino {c |}{col 25}{res}{space 2}        0{col 37}{txt}  (omitted)
{space 18}asian {c |}{col 25}{res}{space 2}        0{col 37}{txt}  (omitted)
{space 12}whitecollar {c |}{col 25}{res}{space 2}  .001069{col 37}{space 2} .0081855{col 48}{space 1}    0.13{col 57}{space 3}0.896{col 65}{space 4}-.0149752{col 78}{space 3} .0171133
{txt}{space 13}bluecollar {c |}{col 25}{res}{space 2}-.0092155{col 37}{space 2} .0079776{col 48}{space 1}   -1.16{col 57}{space 3}0.248{col 65}{space 4}-.0248523{col 78}{space 3} .0064214
{txt}{space 10}serviceworker {c |}{col 25}{res}{space 2}-.0105732{col 37}{space 2} .0059003{col 48}{space 1}   -1.79{col 57}{space 3}0.073{col 65}{space 4}-.0221383{col 78}{space 3} .0009918
{txt}{space 12}mv_thirties {c |}{col 25}{res}{space 2}        0{col 37}{txt}  (omitted)
{space 12}mv_fourties {c |}{col 25}{res}{space 2}        0{col 37}{txt}  (omitted)
{space 13}mv_fifties {c |}{col 25}{res}{space 2}        0{col 37}{txt}  (omitted)
{space 13}mv_sixties {c |}{col 25}{res}{space 2}        0{col 37}{txt}  (omitted)
{space 11}mv_seventies {c |}{col 25}{res}{space 2}        0{col 37}{txt}  (omitted)
{space 5}mv_income2quartile {c |}{col 25}{res}{space 2}        0{col 37}{txt}  (omitted)
{space 5}mv_income3quartile {c |}{col 25}{res}{space 2}        0{col 37}{txt}  (omitted)
{space 5}mv_income4quartile {c |}{col 25}{res}{space 2}        0{col 37}{txt}  (omitted)
{space 6}mv_incomenoanswer {c |}{col 25}{res}{space 2}        0{col 37}{txt}  (omitted)
{space 14}mv_female {c |}{col 25}{res}{space 2}        0{col 37}{txt}  (omitted)
{space 10}mv_highschool {c |}{col 25}{res}{space 2} .0210343{col 37}{space 2} .0120756{col 48}{space 1}    1.74{col 57}{space 3}0.082{col 65}{space 4} -.002635{col 78}{space 3} .0447036
{txt}{space 13}mv_college {c |}{col 25}{res}{space 2}        0{col 37}{txt}  (omitted)
{space 10}mv_noreligion {c |}{col 25}{res}{space 2}  .000241{col 37}{space 2} .0287679{col 48}{space 1}    0.01{col 57}{space 3}0.993{col 65}{space 4}-.0561465{col 78}{space 3} .0566284
{txt}mv_christiannotcatholic {c |}{col 25}{res}{space 2}        0{col 37}{txt}  (omitted)
{space 12}mv_catholic {c |}{col 25}{res}{space 2}        0{col 37}{txt}  (omitted)
{space 6}mv_fulltimeworker {c |}{col 25}{res}{space 2}        0{col 37}{txt}  (omitted)
{space 6}mv_parttimeworker {c |}{col 25}{res}{space 2}-.0085311{col 37}{space 2} .0391255{col 48}{space 1}   -0.22{col 57}{space 3}0.827{col 65}{space 4}-.0852204{col 78}{space 3} .0681582
{txt}{space 10}mv_unemployed {c |}{col 25}{res}{space 2}        0{col 37}{txt}  (omitted)
{space 8}mv_selfemployed {c |}{col 25}{res}{space 2}        0{col 37}{txt}  (omitted)
{space 13}mv_outofLF {c |}{col 25}{res}{space 2}        0{col 37}{txt}  (omitted)
{space 10}mv_goodhealth {c |}{col 25}{res}{space 2} .4903857{col 37}{space 2} .0672719{col 48}{space 1}    7.29{col 57}{space 3}0.000{col 65}{space 4} .3585269{col 78}{space 3} .6222445
{txt}{space 15}mv_white {c |}{col 25}{res}{space 2}-.0060892{col 37}{space 2} .0254218{col 48}{space 1}   -0.24{col 57}{space 3}0.811{col 65}{space 4}-.0559181{col 78}{space 3} .0437396
{txt}{space 15}mv_black {c |}{col 25}{res}{space 2}        0{col 37}{txt}  (omitted)
{space 14}mv_latino {c |}{col 25}{res}{space 2}        0{col 37}{txt}  (omitted)
{space 15}mv_asian {c |}{col 25}{res}{space 2}        0{col 37}{txt}  (omitted)
{space 9}mv_whitecollar {c |}{col 25}{res}{space 2}-.0137181{col 37}{space 2} .0467072{col 48}{space 1}   -0.29{col 57}{space 3}0.769{col 65}{space 4}-.1052682{col 78}{space 3} .0778319
{txt}{space 10}mv_bluecollar {c |}{col 25}{res}{space 2}        0{col 37}{txt}  (omitted)
{space 7}mv_serviceworker {c |}{col 25}{res}{space 2}        0{col 37}{txt}  (omitted)
{space 23} {c |}
{space 16}country {c |}
{space 16}Brazil  {c |}{col 25}{res}{space 2}-.0557551{col 37}{space 2} .0315811{col 48}{space 1}   -1.77{col 57}{space 3}0.078{col 65}{space 4}-.1176567{col 78}{space 3} .0061465
{txt}{space 16}France  {c |}{col 25}{res}{space 2}        0{col 37}{txt}  (omitted)
{space 15}Germany  {c |}{col 25}{res}{space 2}-.0147899{col 37}{space 2} .0141367{col 48}{space 1}   -1.05{col 57}{space 3}0.295{col 65}{space 4} -.042499{col 78}{space 3} .0129193
{txt}{space 17}Italy  {c |}{col 25}{res}{space 2}-.0864972{col 37}{space 2}  .046836{col 48}{space 1}   -1.85{col 57}{space 3}0.065{col 65}{space 4}-.1782998{col 78}{space 3} .0053054
{txt}{space 11}New_Zealand  {c |}{col 25}{res}{space 2}        0{col 37}{txt}  (omitted)
{space 16}Poland  {c |}{col 25}{res}{space 2}-.0976516{col 37}{space 2} .0146082{col 48}{space 1}   -6.68{col 57}{space 3}0.000{col 65}{space 4}-.1262849{col 78}{space 3}-.0690183
{txt}{space 17}Spain  {c |}{col 25}{res}{space 2}        0{col 37}{txt}  (omitted)
{space 16}Sweden  {c |}{col 25}{res}{space 2} .0760169{col 37}{space 2} .0235962{col 48}{space 1}    3.22{col 57}{space 3}0.001{col 65}{space 4} .0297663{col 78}{space 3} .1222675
{txt}{space 20}UK  {c |}{col 25}{res}{space 2}        0{col 37}{txt}  (omitted)
{space 23} {c |}
{space 18}_cons {c |}{col 25}{res}{space 2} .1964617{col 37}{space 2} .1054003{col 48}{space 1}    1.86{col 57}{space 3}0.062{col 65}{space 4}-.0101319{col 78}{space 3} .4030552
{txt}{hline 24}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{help ivreg2##idtest:Underidentification test} (Kleibergen-Paap rk LM statistic):{res}{col 71}   6.400
{txt}{col 52}Chi-sq({res}14{txt}) P-val =  {res}{col 73}0.9554
{txt}{hline 78}
{help ivreg2##widtest:Weak identification test} (Cragg-Donald Wald F statistic):{res}{col 71}   0.426
{txt}                         (Kleibergen-Paap rk Wald F statistic):{res}{col 71}   0.426
{txt}Stock-Yogo weak ID test critical values:{res}{txt}{col 42} 5% maximal IV relative bias{res}{col 73} 19.98
{txt}{col 42}10% maximal IV relative bias{res}{col 73} 10.93
{txt}{col 42}20% maximal IV relative bias{res}{col 73}  6.19
{txt}{col 42}30% maximal IV relative bias{res}{col 73}  4.50
{txt}{col 42}10% maximal IV size{res}{col 73} 38.08
{txt}{col 42}15% maximal IV size{res}{col 73} 20.60
{txt}{col 42}20% maximal IV size{res}{col 73} 14.65
{txt}{col 42}25% maximal IV size{res}{col 73} 11.58
{txt}Source: Stock-Yogo (2005).  Reproduced by permission.
NB: Critical values are for Cragg-Donald F statistic and i.i.d. errors.
{hline 78}
{err}Warning: estimated covariance matrix of moment conditions not of full rank.
         overidentification statistic not reported, and standard errors and
         model tests should be interpreted with caution.
Possible causes:
         singleton dummy variable (dummy with one 1 and N-1 0s or vice versa)
{help ivreg2##partial:partial} option may address problem.
{txt}{hline 78}
Instrumented:{col 23}satis_head satis_reg
Included instruments:{col 23}thirties fourties fifties sixties seventies
{col 23}income2quartile income3quartile income4quartile
{col 23}incomenoanswer female highschool college noreligion
{col 23}christiannotcatholic catholic fulltimeworker
{col 23}parttimeworker unemployed selfemployed outofLF goodhealth
{col 23}whitecollar bluecollar serviceworker mv_highschool
{col 23}mv_noreligion mv_parttimeworker mv_goodhealth mv_white
{col 23}mv_whitecollar 3.country 5.country 6.country 8.country
{col 23}10.country
Excluded instruments:{col 23}TH1_TE1 TH1_TE2 TH1_TE3 TH1_TE4 TH2_TE1 TH2_TE2 TH2_TE3
{col 23}TH2_TE4 TH3_TE1 TH3_TE2 TH3_TE3 TH3_TE4 TH4_TE1 TH4_TE2
{col 23}TH4_TE3
Dropped collinear:{col 23}white black latino asian mv_thirties mv_fourties
{col 23}mv_fifties mv_sixties mv_seventies mv_income2quartile
{col 23}mv_income3quartile mv_income4quartile mv_incomenoanswer
{col 23}mv_female mv_college mv_christiannotcatholic mv_catholic
{col 23}mv_fulltimeworker mv_unemployed mv_selfemployed mv_outofLF
{col 23}mv_black mv_latino mv_asian mv_bluecollar mv_serviceworker
{col 23}4.country 7.country 9.country 11.country
{hline 78}
({res}est6{txt} stored)

{com}. 
. estimates store mod4
{txt}
{com}. estadd scalar fstat = e(widstat)

{txt}added scalar:
              e(fstat) =  {res}.4259662
{txt}
{com}. estadd local CFE "X", replace

{txt}added macro:
                e(CFE) : "{res:X}"

{com}. estadd local Controls "X", replace

{txt}added macro:
           e(Controls) : "{res:X}"

{com}. estadd local IV "16 IVs"

{txt}added macro:
                 e(IV) : "{res:16 IVs}"

{com}. estadd local nothing nothing2 = " "

{txt}added macro:
            e(nothing) : "{res:nothing2 = " "}"

{com}. get_mean

{txt}added scalar:
                 e(av) =  {res}.493
{txt}
{com}. 
. estadd scalar hausman = `temp_hausman'

{txt}added scalar:
            e(hausman) =  {res}1
{txt}
{com}. local coef4 = _b[satis_head]
{txt}
{com}. local se4 = _se[satis_head]
{txt}
{com}. 
. * Z-test 
. local z_value = abs(`coef4'-`coef2')/sqrt(`se4'^2+`se2'^2)
{txt}
{com}. di (1-normal(`z_value'))*2
{res}.692425
{txt}
{com}. estadd scalar wald = (1-normal(`z_value'))*2

{txt}added scalar:
               e(wald) =  {res}.692425
{txt}
{com}. 
. 
. local labgen: variable label satis_dem 
{txt}
{com}. 
. esttab mod1 mod2 mod1r mod2r mod3 mod4 using "3_output/2_OA/Tables/TableE19.tex", depvar keep(satis_head satis_reg ) ///
>         order(satis_head satis_reg serious_h_csqc serious_e_csqc ) label replace ///
>         cells(b(star fmt(%15.3fc)) se(par fmt(%15.3fc))) interaction(" $\times$ ") ///
>         star(* 0.10 ** 0.05 *** 0.01) ///
>         stats(Controls CFE N av IV fstat hausman wald, layout(@ @ @ @ @ @ @ @) fmt(%15s %15s %15.0fc %9.3f %9.3f %9.3f %9.3f %9.3f %9.3f) ///
>         labels("Individual controls" "Country FE" Observations "Outcome mean" "Instruments" "Cragg-Donald statistic" "Hausman test p-value" "Z-test p-value")) ///
>         collabels(none) mlabels(none) ///
>         mgroups("`labgen'" , pattern(1 0 0 0) ///
>         prefix(\multicolumn{c -(}@span{c )-}{c -(}c{c )-}{c -(}) suffix({c )-}) span erepeat(\cmidrule(lr){c -(}@span{c )-}))
{res}{txt}(output written to {browse  `"3_output/2_OA/Tables/TableE19.tex"'})

{com}. 
. restore 
{txt}
{com}. 
{txt}end of do-file

{com}. do "2_code/figures_OA.do"
{txt}
{com}. /*** *** *** *** *** *** *** *** *** *** *** *** *** *** *** *** *** ***
> Author:Nicolas Longuet Marx
> Date: May 5 2021
> Last modified: August 19 2023
> Object: Create figures of evolution of satisfaction levels
> Databases in input: A_experiment.dta and B_panel.dta
> Databases in output: None
> 
> Figures generated in this script:
> 
> - Figure F.1. Aggregate trends in the main variables of interest
> *** *** *** *** *** *** *** *** *** *** *** *** *** *** *** *** *** ****/
. 
. 
. 
. 
. **# Figure 
. 
. set scheme s2mono
{txt}
{com}. grstyle init
{res}{txt}
{com}. grstyle set plain, horizontal grid dotted
{txt}
{com}. 
. use "1_data/A_experiment.dta", replace
{txt}
{com}. 
. gen above_50 = fifties +sixties +seventies
{txt}
{com}. la var college "College degree"
{txt}
{com}. 
. gen income_above_median = income3quartile + income4quartile
{txt}
{com}. 
. /* Health */
. reg eval_sant healthc (c.healthc )#(i.college i.above_50 i.female i.affiliated_gov i.noreligion i.income_above_median )  i.college i.above_50 i.female i.affiliated_gov i.noreligion i.income_above_median , robust

{txt}Linear regression                               Number of obs     = {res}    19,694
                                                {txt}F(13, 19680)      =  {res}   243.74
                                                {txt}Prob > F          = {res}    0.0000
                                                {txt}R-squared         = {res}    0.1140
                                                {txt}Root MSE          =    {res} .25488

{txt}{hline 30}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 31}{c |}{col 43}    Robust
{col 1}                    eval_sant{col 31}{c |} Coefficient{col 43}  std. err.{col 55}      t{col 63}   P>|t|{col 71}     [95% con{col 84}f. interval]
{hline 30}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 22}healthc {c |}{col 31}{res}{space 2} .0130942{col 43}{space 2} .0117561{col 54}{space 1}    1.11{col 63}{space 3}0.265{col 71}{space 4}-.0099488{col 84}{space 3} .0361371
{txt}{space 29} {c |}
{space 12}college#c.healthc {c |}
{space 27}1  {c |}{col 31}{res}{space 2}-.0135984{col 43}{space 2} .0095149{col 54}{space 1}   -1.43{col 63}{space 3}0.153{col 71}{space 4}-.0322484{col 84}{space 3} .0050517
{txt}{space 29} {c |}
{space 11}above_50#c.healthc {c |}
{space 27}1  {c |}{col 31}{res}{space 2} .0106491{col 43}{space 2} .0094549{col 54}{space 1}    1.13{col 63}{space 3}0.260{col 71}{space 4}-.0078833{col 84}{space 3} .0291814
{txt}{space 29} {c |}
{space 13}female#c.healthc {c |}
{space 27}1  {c |}{col 31}{res}{space 2} .0218307{col 43}{space 2}  .009276{col 54}{space 1}    2.35{col 63}{space 3}0.019{col 71}{space 4} .0036489{col 84}{space 3} .0400124
{txt}{space 29} {c |}
{space 5}affiliated_gov#c.healthc {c |}
{space 27}1  {c |}{col 31}{res}{space 2}-.0070134{col 43}{space 2} .0100199{col 54}{space 1}   -0.70{col 63}{space 3}0.484{col 71}{space 4}-.0266533{col 84}{space 3} .0126266
{txt}{space 29} {c |}
{space 9}noreligion#c.healthc {c |}
{space 27}1  {c |}{col 31}{res}{space 2} .0120295{col 43}{space 2} .0094844{col 54}{space 1}    1.27{col 63}{space 3}0.205{col 71}{space 4}-.0065606{col 84}{space 3} .0306197
{txt}{space 29} {c |}
income_above_median#c.healthc {c |}
{space 27}1  {c |}{col 31}{res}{space 2} .0024118{col 43}{space 2} .0093748{col 54}{space 1}    0.26{col 63}{space 3}0.797{col 71}{space 4}-.0159636{col 84}{space 3} .0207873
{txt}{space 29} {c |}
{space 20}1.college {c |}{col 31}{res}{space 2}-.0076869{col 43}{space 2} .0061403{col 54}{space 1}   -1.25{col 63}{space 3}0.211{col 71}{space 4}-.0197224{col 84}{space 3} .0043487
{txt}{space 19}1.above_50 {c |}{col 31}{res}{space 2}-.0249806{col 43}{space 2} .0060595{col 54}{space 1}   -4.12{col 63}{space 3}0.000{col 71}{space 4}-.0368578{col 84}{space 3}-.0131034
{txt}{space 21}1.female {c |}{col 31}{res}{space 2}-.0060771{col 43}{space 2} .0059679{col 54}{space 1}   -1.02{col 63}{space 3}0.309{col 71}{space 4}-.0177748{col 84}{space 3} .0056206
{txt}{space 13}1.affiliated_gov {c |}{col 31}{res}{space 2} .2213925{col 43}{space 2} .0065404{col 54}{space 1}   33.85{col 63}{space 3}0.000{col 71}{space 4} .2085727{col 84}{space 3} .2342123
{txt}{space 17}1.noreligion {c |}{col 31}{res}{space 2}-.0190373{col 43}{space 2} .0061241{col 54}{space 1}   -3.11{col 63}{space 3}0.002{col 71}{space 4}-.0310411{col 84}{space 3}-.0070336
{txt}{space 8}1.income_above_median {c |}{col 31}{res}{space 2} .0133473{col 43}{space 2} .0060496{col 54}{space 1}    2.21{col 63}{space 3}0.027{col 71}{space 4} .0014895{col 84}{space 3} .0252051
{txt}{space 24}_cons {c |}{col 31}{res}{space 2} .4696737{col 43}{space 2} .0074994{col 54}{space 1}   62.63{col 63}{space 3}0.000{col 71}{space 4} .4549742{col 84}{space 3} .4843731
{txt}{hline 30}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{res}{txt}
{com}. 
. capture drop coef_health lb_health ub_health
{txt}
{com}. gen coef_health = . 
{txt}(22,541 missing values generated)

{com}. gen lb_health =  . 
{txt}(22,541 missing values generated)

{com}. gen ub_health =  . 
{txt}(22,541 missing values generated)

{com}. 
. capture drop type
{txt}
{com}. gen type = _n 
{txt}
{com}. 
. lincom 1.noreligion#c.healthc 

{p 0 7}{space 1}{text:( 1)}{space 1} {res}1.noreligion#c.healthc = 0{p_end}

{txt}{hline 13}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 1}   eval_sant{col 14}{c |} Coefficient{col 26}  Std. err.{col 38}      t{col 46}   P>|t|{col 54}     [95% con{col 67}f. interval]
{hline 13}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 9}(1) {c |}{col 14}{res}{space 2} .0120295{col 26}{space 2} .0094844{col 37}{space 1}    1.27{col 46}{space 3}0.205{col 54}{space 4}-.0065606{col 67}{space 3} .0306197
{txt}{hline 13}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}

{com}. qui replace coef_health = `r(estimate)' if type == 1
{txt}
{com}. qui replace lb_health = `r(lb)' if type == 1
{txt}
{com}. qui replace ub_health = `r(ub)' if type == 1
{txt}
{com}. 
. lincom 1.above_50#c.healthc 

{p 0 7}{space 1}{text:( 1)}{space 1} {res}1.above_50#c.healthc = 0{p_end}

{txt}{hline 13}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 1}   eval_sant{col 14}{c |} Coefficient{col 26}  Std. err.{col 38}      t{col 46}   P>|t|{col 54}     [95% con{col 67}f. interval]
{hline 13}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 9}(1) {c |}{col 14}{res}{space 2} .0106491{col 26}{space 2} .0094549{col 37}{space 1}    1.13{col 46}{space 3}0.260{col 54}{space 4}-.0078833{col 67}{space 3} .0291814
{txt}{hline 13}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}

{com}. qui replace coef_health = `r(estimate)' if type == 2
{txt}
{com}. qui replace lb_health = `r(lb)' if type == 2
{txt}
{com}. qui replace ub_health = `r(ub)' if type == 2
{txt}
{com}. 
. lincom 1.female#c.healthc 

{p 0 7}{space 1}{text:( 1)}{space 1} {res}1.female#c.healthc = 0{p_end}

{txt}{hline 13}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 1}   eval_sant{col 14}{c |} Coefficient{col 26}  Std. err.{col 38}      t{col 46}   P>|t|{col 54}     [95% con{col 67}f. interval]
{hline 13}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 9}(1) {c |}{col 14}{res}{space 2} .0218307{col 26}{space 2}  .009276{col 37}{space 1}    2.35{col 46}{space 3}0.019{col 54}{space 4} .0036489{col 67}{space 3} .0400124
{txt}{hline 13}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}

{com}. qui replace coef_health = `r(estimate)' if type == 3
{txt}
{com}. qui replace lb_health = `r(lb)' if type == 3
{txt}
{com}. qui replace ub_health = `r(ub)' if type == 3
{txt}
{com}. 
. lincom 1.affiliated_gov#c.healthc 

{p 0 7}{space 1}{text:( 1)}{space 1} {res}1.affiliated_gov#c.healthc = 0{p_end}

{txt}{hline 13}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 1}   eval_sant{col 14}{c |} Coefficient{col 26}  Std. err.{col 38}      t{col 46}   P>|t|{col 54}     [95% con{col 67}f. interval]
{hline 13}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 9}(1) {c |}{col 14}{res}{space 2}-.0070134{col 26}{space 2} .0100199{col 37}{space 1}   -0.70{col 46}{space 3}0.484{col 54}{space 4}-.0266533{col 67}{space 3} .0126266
{txt}{hline 13}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}

{com}. qui replace coef_health = `r(estimate)' if type == 4
{txt}
{com}. qui replace lb_health = `r(lb)' if type == 4
{txt}
{com}. qui replace ub_health = `r(ub)' if type == 4
{txt}
{com}. 
. lincom 1.income_above_median#c.healthc 

{p 0 7}{space 1}{text:( 1)}{space 1} {res}1.income_above_median#c.healthc = 0{p_end}

{txt}{hline 13}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 1}   eval_sant{col 14}{c |} Coefficient{col 26}  Std. err.{col 38}      t{col 46}   P>|t|{col 54}     [95% con{col 67}f. interval]
{hline 13}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 9}(1) {c |}{col 14}{res}{space 2} .0024118{col 26}{space 2} .0093748{col 37}{space 1}    0.26{col 46}{space 3}0.797{col 54}{space 4}-.0159636{col 67}{space 3} .0207873
{txt}{hline 13}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}

{com}. qui replace coef_health = `r(estimate)' if type == 5
{txt}
{com}. qui replace lb_health= `r(lb)' if type == 5
{txt}
{com}. qui replace ub_health = `r(ub)' if type == 5
{txt}
{com}. 
. lincom 1.college#c.healthc 

{p 0 7}{space 1}{text:( 1)}{space 1} {res}1.college#c.healthc = 0{p_end}

{txt}{hline 13}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 1}   eval_sant{col 14}{c |} Coefficient{col 26}  Std. err.{col 38}      t{col 46}   P>|t|{col 54}     [95% con{col 67}f. interval]
{hline 13}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 9}(1) {c |}{col 14}{res}{space 2}-.0135984{col 26}{space 2} .0095149{col 37}{space 1}   -1.43{col 46}{space 3}0.153{col 54}{space 4}-.0322484{col 67}{space 3} .0050517
{txt}{hline 13}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}

{com}. qui replace coef_health = `r(estimate)' if type == 6
{txt}
{com}. qui replace lb_health = `r(lb)' if type == 6
{txt}
{com}. qui replace ub_health = `r(ub)' if type == 6
{txt}
{com}. 
. 
. /* Econ */
. reg eval_eco econc (c.econc )#(i.college i.above_50 i.female i.affiliated_gov i.noreligion i.income_above_median )  i.college i.above_50 i.female i.affiliated_gov i.noreligion i.income_above_median , robust

{txt}Linear regression                               Number of obs     = {res}    19,694
                                                {txt}F(13, 19680)      =  {res}   219.85
                                                {txt}Prob > F          = {res}    0.0000
                                                {txt}R-squared         = {res}    0.1076
                                                {txt}Root MSE          =    {res} .24427

{txt}{hline 28}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 29}{c |}{col 41}    Robust
{col 1}                   eval_eco{col 29}{c |} Coefficient{col 41}  std. err.{col 53}      t{col 61}   P>|t|{col 69}     [95% con{col 82}f. interval]
{hline 28}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 22}econc {c |}{col 29}{res}{space 2}  .001817{col 41}{space 2} .0114782{col 52}{space 1}    0.16{col 61}{space 3}0.874{col 69}{space 4}-.0206813{col 82}{space 3} .0243153
{txt}{space 27} {c |}
{space 12}college#c.econc {c |}
{space 25}1  {c |}{col 29}{res}{space 2} .0108376{col 41}{space 2} .0091623{col 52}{space 1}    1.18{col 61}{space 3}0.237{col 69}{space 4}-.0071213{col 82}{space 3} .0287964
{txt}{space 27} {c |}
{space 11}above_50#c.econc {c |}
{space 25}1  {c |}{col 29}{res}{space 2} .0032056{col 41}{space 2} .0090931{col 52}{space 1}    0.35{col 61}{space 3}0.724{col 69}{space 4}-.0146177{col 82}{space 3} .0210289
{txt}{space 27} {c |}
{space 13}female#c.econc {c |}
{space 25}1  {c |}{col 29}{res}{space 2} .0139377{col 41}{space 2} .0089216{col 52}{space 1}    1.56{col 61}{space 3}0.118{col 69}{space 4}-.0035494{col 82}{space 3} .0314248
{txt}{space 27} {c |}
{space 5}affiliated_gov#c.econc {c |}
{space 25}1  {c |}{col 29}{res}{space 2} .0115589{col 41}{space 2}  .009966{col 52}{space 1}    1.16{col 61}{space 3}0.246{col 69}{space 4}-.0079753{col 82}{space 3} .0310932
{txt}{space 27} {c |}
{space 9}noreligion#c.econc {c |}
{space 25}1  {c |}{col 29}{res}{space 2}-.0033373{col 41}{space 2} .0090691{col 52}{space 1}   -0.37{col 61}{space 3}0.713{col 69}{space 4}-.0211134{col 82}{space 3} .0144389
{txt}{space 27} {c |}
income_above_median#c.econc {c |}
{space 25}1  {c |}{col 29}{res}{space 2} .0020157{col 41}{space 2} .0090179{col 52}{space 1}    0.22{col 61}{space 3}0.823{col 69}{space 4}-.0156601{col 82}{space 3} .0196916
{txt}{space 27} {c |}
{space 18}1.college {c |}{col 29}{res}{space 2}-.0202115{col 41}{space 2} .0058433{col 52}{space 1}   -3.46{col 61}{space 3}0.001{col 69}{space 4}-.0316649{col 82}{space 3}-.0087581
{txt}{space 17}1.above_50 {c |}{col 29}{res}{space 2} -.006747{col 41}{space 2}  .005802{col 52}{space 1}   -1.16{col 61}{space 3}0.245{col 69}{space 4}-.0181195{col 82}{space 3} .0046255
{txt}{space 19}1.female {c |}{col 29}{res}{space 2} -.005511{col 41}{space 2} .0057236{col 52}{space 1}   -0.96{col 61}{space 3}0.336{col 69}{space 4}-.0167297{col 82}{space 3} .0057077
{txt}{space 11}1.affiliated_gov {c |}{col 29}{res}{space 2} .1953753{col 41}{space 2} .0063976{col 52}{space 1}   30.54{col 61}{space 3}0.000{col 69}{space 4} .1828354{col 82}{space 3} .2079152
{txt}{space 15}1.noreligion {c |}{col 29}{res}{space 2}-.0121338{col 41}{space 2} .0058115{col 52}{space 1}   -2.09{col 61}{space 3}0.037{col 69}{space 4}-.0235249{col 82}{space 3}-.0007427
{txt}{space 6}1.income_above_median {c |}{col 29}{res}{space 2} .0191089{col 41}{space 2} .0057549{col 52}{space 1}    3.32{col 61}{space 3}0.001{col 69}{space 4} .0078289{col 82}{space 3} .0303889
{txt}{space 22}_cons {c |}{col 29}{res}{space 2} .4672566{col 41}{space 2} .0073462{col 52}{space 1}   63.60{col 61}{space 3}0.000{col 69}{space 4} .4528574{col 82}{space 3} .4816558
{txt}{hline 28}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{res}{txt}
{com}. 
. capture drop coef_eco lb_eco ub_eco
{txt}
{com}. gen coef_eco = . 
{txt}(22,541 missing values generated)

{com}. gen lb_eco =  . 
{txt}(22,541 missing values generated)

{com}. gen ub_eco =  . 
{txt}(22,541 missing values generated)

{com}. capture drop type_eco
{txt}
{com}. gen type_eco = _n
{txt}
{com}. 
. lincom 1.noreligion#c.econc 

{p 0 7}{space 1}{text:( 1)}{space 1} {res}1.noreligion#c.econc = 0{p_end}

{txt}{hline 13}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 1}    eval_eco{col 14}{c |} Coefficient{col 26}  Std. err.{col 38}      t{col 46}   P>|t|{col 54}     [95% con{col 67}f. interval]
{hline 13}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 9}(1) {c |}{col 14}{res}{space 2}-.0033373{col 26}{space 2} .0090691{col 37}{space 1}   -0.37{col 46}{space 3}0.713{col 54}{space 4}-.0211134{col 67}{space 3} .0144389
{txt}{hline 13}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}

{com}. qui replace coef_eco = `r(estimate)' if type_eco == 1
{txt}
{com}. qui replace lb_eco = `r(lb)' if type_eco == 1
{txt}
{com}. qui replace ub_eco = `r(ub)' if type_eco == 1
{txt}
{com}. 
. lincom 1.above_50#c.econc 

{p 0 7}{space 1}{text:( 1)}{space 1} {res}1.above_50#c.econc = 0{p_end}

{txt}{hline 13}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 1}    eval_eco{col 14}{c |} Coefficient{col 26}  Std. err.{col 38}      t{col 46}   P>|t|{col 54}     [95% con{col 67}f. interval]
{hline 13}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 9}(1) {c |}{col 14}{res}{space 2} .0032056{col 26}{space 2} .0090931{col 37}{space 1}    0.35{col 46}{space 3}0.724{col 54}{space 4}-.0146177{col 67}{space 3} .0210289
{txt}{hline 13}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}

{com}. qui replace coef_eco = `r(estimate)' if type_eco == 2
{txt}
{com}. qui replace lb_eco = `r(lb)' if type_eco == 2
{txt}
{com}. qui replace ub_eco = `r(ub)' if type_eco == 2
{txt}
{com}. 
. lincom 1.female#c.econc 

{p 0 7}{space 1}{text:( 1)}{space 1} {res}1.female#c.econc = 0{p_end}

{txt}{hline 13}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 1}    eval_eco{col 14}{c |} Coefficient{col 26}  Std. err.{col 38}      t{col 46}   P>|t|{col 54}     [95% con{col 67}f. interval]
{hline 13}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 9}(1) {c |}{col 14}{res}{space 2} .0139377{col 26}{space 2} .0089216{col 37}{space 1}    1.56{col 46}{space 3}0.118{col 54}{space 4}-.0035494{col 67}{space 3} .0314248
{txt}{hline 13}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}

{com}. qui replace coef_eco = `r(estimate)' if type_eco == 3
{txt}
{com}. qui replace lb_eco = `r(lb)' if type_eco == 3
{txt}
{com}. qui replace ub_eco = `r(ub)' if type_eco == 3
{txt}
{com}. 
. lincom 1.affiliated_gov#c.econc 

{p 0 7}{space 1}{text:( 1)}{space 1} {res}1.affiliated_gov#c.econc = 0{p_end}

{txt}{hline 13}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 1}    eval_eco{col 14}{c |} Coefficient{col 26}  Std. err.{col 38}      t{col 46}   P>|t|{col 54}     [95% con{col 67}f. interval]
{hline 13}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 9}(1) {c |}{col 14}{res}{space 2} .0115589{col 26}{space 2}  .009966{col 37}{space 1}    1.16{col 46}{space 3}0.246{col 54}{space 4}-.0079753{col 67}{space 3} .0310932
{txt}{hline 13}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}

{com}. qui replace coef_eco = `r(estimate)' if type_eco == 4
{txt}
{com}. qui replace lb_eco = `r(lb)' if type_eco == 4
{txt}
{com}. qui replace ub_eco = `r(ub)' if type_eco == 4
{txt}
{com}. 
. lincom 1.income_above_median#c.econc 

{p 0 7}{space 1}{text:( 1)}{space 1} {res}1.income_above_median#c.econc = 0{p_end}

{txt}{hline 13}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 1}    eval_eco{col 14}{c |} Coefficient{col 26}  Std. err.{col 38}      t{col 46}   P>|t|{col 54}     [95% con{col 67}f. interval]
{hline 13}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 9}(1) {c |}{col 14}{res}{space 2} .0020157{col 26}{space 2} .0090179{col 37}{space 1}    0.22{col 46}{space 3}0.823{col 54}{space 4}-.0156601{col 67}{space 3} .0196916
{txt}{hline 13}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}

{com}. qui replace coef_eco = `r(estimate)' if type_eco == 5
{txt}
{com}. qui replace lb_eco = `r(lb)' if type_eco == 5
{txt}
{com}. qui replace ub_eco = `r(ub)' if type_eco == 5
{txt}
{com}. 
. lincom 1.college#c.econc 

{p 0 7}{space 1}{text:( 1)}{space 1} {res}1.college#c.econc = 0{p_end}

{txt}{hline 13}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 1}    eval_eco{col 14}{c |} Coefficient{col 26}  Std. err.{col 38}      t{col 46}   P>|t|{col 54}     [95% con{col 67}f. interval]
{hline 13}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 9}(1) {c |}{col 14}{res}{space 2} .0108376{col 26}{space 2} .0091623{col 37}{space 1}    1.18{col 46}{space 3}0.237{col 54}{space 4}-.0071213{col 67}{space 3} .0287964
{txt}{hline 13}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}

{com}. qui replace coef_eco = `r(estimate)' if type_eco == 6
{txt}
{com}. qui replace lb_eco = `r(lb)' if type_eco == 6
{txt}
{com}. qui replace ub_eco = `r(ub)' if type_eco == 6
{txt}
{com}. 
. replace type_eco = type_eco-0.2
{txt}(22,541 real changes made)

{com}. 
. 
. /* Satisfaction with the head of government */
. reg satis_head treatc (c.treatc )#(i.college i.above_50 i.female i.affiliated_gov i.noreligion i.income_above_median )  i.college i.above_50 i.female i.affiliated_gov i.noreligion i.income_above_median , robust

{txt}Linear regression                               Number of obs     = {res}    19,694
                                                {txt}F(13, 19680)      =  {res}   461.16
                                                {txt}Prob > F          = {res}    0.0000
                                                {txt}R-squared         = {res}    0.1954
                                                {txt}Root MSE          =    {res} .28301

{txt}{hline 29}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 30}{c |}{col 42}    Robust
{col 1}                  satis_head{col 30}{c |} Coefficient{col 42}  std. err.{col 54}      t{col 62}   P>|t|{col 70}     [95% con{col 83}f. interval]
{hline 29}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 22}treatc {c |}{col 30}{res}{space 2}  .011699{col 42}{space 2} .0092448{col 53}{space 1}    1.27{col 62}{space 3}0.206{col 70}{space 4}-.0064216{col 83}{space 3} .0298196
{txt}{space 28} {c |}
{space 12}college#c.treatc {c |}
{space 26}1  {c |}{col 30}{res}{space 2}-.0193935{col 42}{space 2} .0074676{col 53}{space 1}   -2.60{col 62}{space 3}0.009{col 70}{space 4}-.0340306{col 83}{space 3}-.0047564
{txt}{space 28} {c |}
{space 11}above_50#c.treatc {c |}
{space 26}1  {c |}{col 30}{res}{space 2} .0037968{col 42}{space 2} .0074095{col 53}{space 1}    0.51{col 62}{space 3}0.608{col 70}{space 4}-.0107265{col 83}{space 3} .0183201
{txt}{space 28} {c |}
{space 13}female#c.treatc {c |}
{space 26}1  {c |}{col 30}{res}{space 2} .0044371{col 42}{space 2} .0072734{col 53}{space 1}    0.61{col 62}{space 3}0.542{col 70}{space 4}-.0098194{col 83}{space 3} .0186935
{txt}{space 28} {c |}
{space 5}affiliated_gov#c.treatc {c |}
{space 26}1  {c |}{col 30}{res}{space 2}-.0067494{col 42}{space 2} .0078437{col 53}{space 1}   -0.86{col 62}{space 3}0.390{col 70}{space 4}-.0221237{col 83}{space 3} .0086249
{txt}{space 28} {c |}
{space 9}noreligion#c.treatc {c |}
{space 26}1  {c |}{col 30}{res}{space 2} .0160998{col 42}{space 2} .0074803{col 53}{space 1}    2.15{col 62}{space 3}0.031{col 70}{space 4} .0014379{col 83}{space 3} .0307618
{txt}{space 28} {c |}
income_above_median#c.treatc {c |}
{space 26}1  {c |}{col 30}{res}{space 2}-.0025672{col 42}{space 2} .0073595{col 53}{space 1}   -0.35{col 62}{space 3}0.727{col 70}{space 4}-.0169925{col 83}{space 3} .0118581
{txt}{space 28} {c |}
{space 19}1.college {c |}{col 30}{res}{space 2}-.0212804{col 42}{space 2} .0041965{col 53}{space 1}   -5.07{col 62}{space 3}0.000{col 70}{space 4}-.0295059{col 83}{space 3} -.013055
{txt}{space 18}1.above_50 {c |}{col 30}{res}{space 2}-.0406106{col 42}{space 2}  .004164{col 53}{space 1}   -9.75{col 62}{space 3}0.000{col 70}{space 4}-.0487724{col 83}{space 3}-.0324487
{txt}{space 20}1.female {c |}{col 30}{res}{space 2} .0138377{col 42}{space 2} .0040825{col 53}{space 1}    3.39{col 62}{space 3}0.001{col 70}{space 4} .0058358{col 83}{space 3} .0218397
{txt}{space 12}1.affiliated_gov {c |}{col 30}{res}{space 2} .3344317{col 42}{space 2} .0044225{col 53}{space 1}   75.62{col 62}{space 3}0.000{col 70}{space 4} .3257632{col 83}{space 3} .3431001
{txt}{space 16}1.noreligion {c |}{col 30}{res}{space 2}-.0243969{col 42}{space 2} .0041878{col 53}{space 1}   -5.83{col 62}{space 3}0.000{col 70}{space 4}-.0326053{col 83}{space 3}-.0161884
{txt}{space 7}1.income_above_median {c |}{col 30}{res}{space 2} .0156471{col 42}{space 2} .0041298{col 53}{space 1}    3.79{col 62}{space 3}0.000{col 70}{space 4} .0075524{col 83}{space 3} .0237417
{txt}{space 23}_cons {c |}{col 30}{res}{space 2} .4126345{col 42}{space 2} .0052322{col 53}{space 1}   78.86{col 62}{space 3}0.000{col 70}{space 4} .4023789{col 83}{space 3} .4228902
{txt}{hline 29}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{res}{txt}
{com}. 
. capture drop coef lb ub
{txt}
{com}. gen coef = . 
{txt}(22,541 missing values generated)

{com}. gen lb =  . 
{txt}(22,541 missing values generated)

{com}. gen ub =  . 
{txt}(22,541 missing values generated)

{com}. capture drop type_general
{txt}
{com}. gen type_general = _n 
{txt}
{com}. 
. lincom 1.noreligion#c.treatc 

{p 0 7}{space 1}{text:( 1)}{space 1} {res}1.noreligion#c.treatc = 0{p_end}

{txt}{hline 13}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 1}  satis_head{col 14}{c |} Coefficient{col 26}  Std. err.{col 38}      t{col 46}   P>|t|{col 54}     [95% con{col 67}f. interval]
{hline 13}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 9}(1) {c |}{col 14}{res}{space 2} .0160998{col 26}{space 2} .0074803{col 37}{space 1}    2.15{col 46}{space 3}0.031{col 54}{space 4} .0014379{col 67}{space 3} .0307618
{txt}{hline 13}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}

{com}. qui replace coef = `r(estimate)' if type_general == 1
{txt}
{com}. qui replace lb = `r(lb)' if type_general == 1
{txt}
{com}. qui replace ub = `r(ub)' if type_general == 1
{txt}
{com}. 
. lincom 1.above_50#c.treatc 

{p 0 7}{space 1}{text:( 1)}{space 1} {res}1.above_50#c.treatc = 0{p_end}

{txt}{hline 13}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 1}  satis_head{col 14}{c |} Coefficient{col 26}  Std. err.{col 38}      t{col 46}   P>|t|{col 54}     [95% con{col 67}f. interval]
{hline 13}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 9}(1) {c |}{col 14}{res}{space 2} .0037968{col 26}{space 2} .0074095{col 37}{space 1}    0.51{col 46}{space 3}0.608{col 54}{space 4}-.0107265{col 67}{space 3} .0183201
{txt}{hline 13}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}

{com}. qui replace coef = `r(estimate)' if type_general == 2
{txt}
{com}. qui replace lb = `r(lb)' if type_general == 2
{txt}
{com}. qui replace ub = `r(ub)' if type_general == 2
{txt}
{com}. 
. lincom 1.female#c.treatc 

{p 0 7}{space 1}{text:( 1)}{space 1} {res}1.female#c.treatc = 0{p_end}

{txt}{hline 13}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 1}  satis_head{col 14}{c |} Coefficient{col 26}  Std. err.{col 38}      t{col 46}   P>|t|{col 54}     [95% con{col 67}f. interval]
{hline 13}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 9}(1) {c |}{col 14}{res}{space 2} .0044371{col 26}{space 2} .0072734{col 37}{space 1}    0.61{col 46}{space 3}0.542{col 54}{space 4}-.0098194{col 67}{space 3} .0186935
{txt}{hline 13}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}

{com}. qui replace coef = `r(estimate)' if type_general == 3
{txt}
{com}. qui replace lb = `r(lb)' if type_general == 3
{txt}
{com}. qui replace ub = `r(ub)' if type_general == 3
{txt}
{com}. 
. lincom 1.affiliated_gov#c.treatc 

{p 0 7}{space 1}{text:( 1)}{space 1} {res}1.affiliated_gov#c.treatc = 0{p_end}

{txt}{hline 13}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 1}  satis_head{col 14}{c |} Coefficient{col 26}  Std. err.{col 38}      t{col 46}   P>|t|{col 54}     [95% con{col 67}f. interval]
{hline 13}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 9}(1) {c |}{col 14}{res}{space 2}-.0067494{col 26}{space 2} .0078437{col 37}{space 1}   -0.86{col 46}{space 3}0.390{col 54}{space 4}-.0221237{col 67}{space 3} .0086249
{txt}{hline 13}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}

{com}. qui replace coef = `r(estimate)' if type_general == 4
{txt}
{com}. qui replace lb = `r(lb)' if type_general == 4
{txt}
{com}. qui replace ub = `r(ub)' if type_general == 4
{txt}
{com}. 
. lincom 1.income_above_median#c.treatc 

{p 0 7}{space 1}{text:( 1)}{space 1} {res}1.income_above_median#c.treatc = 0{p_end}

{txt}{hline 13}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 1}  satis_head{col 14}{c |} Coefficient{col 26}  Std. err.{col 38}      t{col 46}   P>|t|{col 54}     [95% con{col 67}f. interval]
{hline 13}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 9}(1) {c |}{col 14}{res}{space 2}-.0025672{col 26}{space 2} .0073595{col 37}{space 1}   -0.35{col 46}{space 3}0.727{col 54}{space 4}-.0169925{col 67}{space 3} .0118581
{txt}{hline 13}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}

{com}. qui replace coef = `r(estimate)' if type_general == 5
{txt}
{com}. qui replace lb = `r(lb)' if type_general == 5
{txt}
{com}. qui replace ub = `r(ub)' if type_general == 5
{txt}
{com}. 
. lincom 1.college#c.treatc 

{p 0 7}{space 1}{text:( 1)}{space 1} {res}1.college#c.treatc = 0{p_end}

{txt}{hline 13}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 1}  satis_head{col 14}{c |} Coefficient{col 26}  Std. err.{col 38}      t{col 46}   P>|t|{col 54}     [95% con{col 67}f. interval]
{hline 13}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 9}(1) {c |}{col 14}{res}{space 2}-.0193935{col 26}{space 2} .0074676{col 37}{space 1}   -2.60{col 46}{space 3}0.009{col 54}{space 4}-.0340306{col 67}{space 3}-.0047564
{txt}{hline 13}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}

{com}. qui replace coef = `r(estimate)' if type_general == 6
{txt}
{com}. qui replace lb = `r(lb)' if type_general == 6
{txt}
{com}. qui replace ub = `r(ub)' if type_general == 6
{txt}
{com}. 
. replace type_general = type_general-0.4
{txt}(22,541 real changes made)

{com}. 
. 
. preserve 
{txt}
{com}. drop if coef ==. 
{txt}(22,535 observations deleted)

{com}. tw (scatter type coef_health, color(green) m(dh)) (rcap  lb_health ub_health type, horizontal color(green%50)) ///
> (scatter type_eco coef_eco, color(orange_red) m(sh)) (rcap  lb_eco ub_eco type_eco, horizontal color(orange_red%50)) ///
> (scatter type_general coef, color(midblue) m(Oh)) (rcap  lb ub type_general, horizontal color(midblue%50))  ,  legend( col(3)   size(small) order(1 "Health" 3 "Economic" 5 "Head of Goverment")) yla(1 "No religion" 2 "Above 50 y.o." 3 "Female" 4 "Affiliated with gov" 5 "Income above median" 6 "College graduate" , nogrid) ytitle("") xtitle("Coefficient on interaction") xline(0) xla(,nogrid)
{res}{txt}
{com}. graph export "3_output/2_OA/Figures/FigureE1.pdf", as(pdf) name("Graph") replace
{txt}{p 0 4 2}
file {bf}
3_output/2_OA/Figures/FigureE1.pdf{rm}
saved as
PDF
format
{p_end}

{com}. restore
{txt}
{com}. 
. 
. 
. 
. 
. 
. 
. 
. 
. 
. 
. 
. /* Graph with all countries together */ 
. set scheme plotplainblind
{txt}
{com}. 
. use "1_data/B_panel.dta", replace
{txt}
{com}. 
. 
. /* we give equal weights to each country */
. gen weights = .
{txt}(52,391 missing values generated)

{com}. levelsof country, local(levels)
{res}{txt}`"Australia"' `"Austria"' `"France"' `"Germany"' `"Italy"' `"New_Zealand"' `"UK"' `"US"'

{com}. foreach l of local levels {c -(}
{txt}  2{com}.         forval n = 1/4 {c -(}
{txt}  3{com}.                 count if country == "`l'" & wave == `n'
{txt}  4{com}.                 replace weights = 1/r(N) if country == "`l'"  & wave == `n'
{txt}  5{com}.         {c )-}
{txt}  6{com}. {c )-}
  {res}1,003
{txt}(1,003 real changes made)
  {res}1,007
{txt}(1,007 real changes made)
  {res}1,003
{txt}(1,003 real changes made)
  {res}1,010
{txt}(1,010 real changes made)
  {res}1,000
{txt}(1,000 real changes made)
  {res}1,000
{txt}(1,000 real changes made)
  {res}1,011
{txt}(1,011 real changes made)
  {res}1,000
{txt}(1,000 real changes made)
  {res}1,999
{txt}(1,999 real changes made)
  {res}2,020
{txt}(2,020 real changes made)
  {res}2,007
{txt}(2,007 real changes made)
  {res}10,686
{txt}(10,686 real changes made)
  {res}1,501
{txt}(1,501 real changes made)
  {res}2,000
{txt}(2,000 real changes made)
  {res}2,016
{txt}(2,016 real changes made)
  {res}2,001
{txt}(2,001 real changes made)
  {res}1,000
{txt}(1,000 real changes made)
  {res}997
{txt}(997 real changes made)
  {res}1,003
{txt}(1,003 real changes made)
  {res}1,000
{txt}(1,000 real changes made)
  {res}999
{txt}(999 real changes made)
  {res}998
{txt}(998 real changes made)
  {res}1,000
{txt}(1,000 real changes made)
  {res}1,000
{txt}(1,000 real changes made)
  {res}1,011
{txt}(1,011 real changes made)
  {res}1,000
{txt}(1,000 real changes made)
  {res}1,014
{txt}(1,014 real changes made)
  {res}1,000
{txt}(1,000 real changes made)
  {res}2,089
{txt}(2,089 real changes made)
  {res}2,007
{txt}(2,007 real changes made)
  {res}2,003
{txt}(2,003 real changes made)
  {res}2,006
{txt}(2,006 real changes made)

{com}. foreach var of varlist satis_head satis_dem {c -(}
{txt}  2{com}.         reg `var' i.wave [aw = weights]
{txt}  3{com}. 
.         gen coef_`var' = . 
{txt}  4{com}.         gen cimin_`var' = .
{txt}  5{com}.         gen cimax_`var' = .
{txt}  6{com}. 
. 
.         levelsof wave, local(l_wave) 
{txt}  7{com}.         foreach l of local l_wave {c -(}
{txt}  8{com}.                 qui count if `var' !=. & wave == `l' 
{txt}  9{com}.                 if r(N)!=0 {c -(}
{txt} 10{com}.                         lincom _cons+`l'.wave
{txt} 11{com}.                         replace coef_`var' = r(estimate) if wave == `l'
{txt} 12{com}.                         replace cimin_`var' = r(lb) if wave == `l' 
{txt} 13{com}.                         replace cimax_`var' = r(ub) if wave == `l'
{txt} 14{com}.                 {c )-}
{txt} 15{com}.         {c )-}
{txt} 16{com}. {c )-}               
{txt}(sum of wgt is 32.00000072186231)

      Source {c |}       SS           df       MS      Number of obs   ={res}    52,391
{txt}{hline 13}{c +}{hline 34}   F(3, 52387)     = {res}    63.51
{txt}       Model {c |} {res} 18.9902966         3  6.33009885   {txt}Prob > F        ={res}    0.0000
{txt}    Residual {c |} {res} 5221.40508    52,387  .099669862   {txt}R-squared       ={res}    0.0036
{txt}{hline 13}{c +}{hline 34}   Adj R-squared   ={res}    0.0036
{txt}       Total {c |} {res} 5240.39538    52,390  .100026634   {txt}Root MSE        =   {res} .31571

{txt}{hline 13}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 1}  satis_head{col 14}{c |} Coefficient{col 26}  Std. err.{col 38}      t{col 46}   P>|t|{col 54}     [95% con{col 67}f. interval]
{hline 13}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 8}wave {c |}
{space 10}2  {c |}{col 14}{res}{space 2} .0177106{col 26}{space 2} .0039012{col 37}{space 1}    4.54{col 46}{space 3}0.000{col 54}{space 4} .0100642{col 67}{space 3}  .025357
{txt}{space 10}3  {c |}{col 14}{res}{space 2}-.0157828{col 26}{space 2} .0039012{col 37}{space 1}   -4.05{col 46}{space 3}0.000{col 54}{space 4}-.0234292{col 67}{space 3}-.0081364
{txt}{space 10}4  {c |}{col 14}{res}{space 2}-.0337738{col 26}{space 2} .0039012{col 37}{space 1}   -8.66{col 46}{space 3}0.000{col 54}{space 4}-.0414202{col 67}{space 3}-.0261274
{txt}{space 12} {c |}
{space 7}_cons {c |}{col 14}{res}{space 2} .5637224{col 26}{space 2} .0027586{col 37}{space 1}  204.35{col 46}{space 3}0.000{col 54}{space 4} .5583156{col 67}{space 3} .5691293
{txt}{hline 13}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{res}{txt}(52,391 missing values generated)
(52,391 missing values generated)
(52,391 missing values generated)
{res}{txt}1 2 3 4

{p 0 7}{space 1}{text:( 1)}{space 1} {res}1b.wave + _cons = 0{p_end}

{txt}{hline 13}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 1}  satis_head{col 14}{c |} Coefficient{col 26}  Std. err.{col 38}      t{col 46}   P>|t|{col 54}     [95% con{col 67}f. interval]
{hline 13}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 9}(1) {c |}{col 14}{res}{space 2} .5637224{col 26}{space 2} .0027586{col 37}{space 1}  204.35{col 46}{space 3}0.000{col 54}{space 4} .5583156{col 67}{space 3} .5691293
{txt}{hline 13}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
(10,602 real changes made)
(10,602 real changes made)
(10,602 real changes made)

{p 0 7}{space 1}{text:( 1)}{space 1} {res}2.wave + _cons = 0{p_end}

{txt}{hline 13}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 1}  satis_head{col 14}{c |} Coefficient{col 26}  Std. err.{col 38}      t{col 46}   P>|t|{col 54}     [95% con{col 67}f. interval]
{hline 13}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 9}(1) {c |}{col 14}{res}{space 2}  .581433{col 26}{space 2} .0027586{col 37}{space 1}  210.77{col 46}{space 3}0.000{col 54}{space 4} .5760262{col 67}{space 3} .5868398
{txt}{hline 13}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
(11,029 real changes made)
(11,029 real changes made)
(11,029 real changes made)

{p 0 7}{space 1}{text:( 1)}{space 1} {res}3.wave + _cons = 0{p_end}

{txt}{hline 13}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 1}  satis_head{col 14}{c |} Coefficient{col 26}  Std. err.{col 38}      t{col 46}   P>|t|{col 54}     [95% con{col 67}f. interval]
{hline 13}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 9}(1) {c |}{col 14}{res}{space 2} .5479396{col 26}{space 2} .0027586{col 37}{space 1}  198.63{col 46}{space 3}0.000{col 54}{space 4} .5425328{col 67}{space 3} .5533464
{txt}{hline 13}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
(11,057 real changes made)
(11,057 real changes made)
(11,057 real changes made)

{p 0 7}{space 1}{text:( 1)}{space 1} {res}4.wave + _cons = 0{p_end}

{txt}{hline 13}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 1}  satis_head{col 14}{c |} Coefficient{col 26}  Std. err.{col 38}      t{col 46}   P>|t|{col 54}     [95% con{col 67}f. interval]
{hline 13}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 9}(1) {c |}{col 14}{res}{space 2} .5299486{col 26}{space 2} .0027586{col 37}{space 1}  192.11{col 46}{space 3}0.000{col 54}{space 4} .5245418{col 67}{space 3} .5353554
{txt}{hline 13}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
(19,703 real changes made)
(19,703 real changes made)
(19,703 real changes made)
(sum of wgt is 24.00000061382889)

      Source {c |}       SS           df       MS      Number of obs   ={res}    41,789
{txt}{hline 13}{c +}{hline 34}   F(2, 41786)     = {res}    85.49
{txt}       Model {c |} {res}  11.297312         2  5.64865602   {txt}Prob > F        ={res}    0.0000
{txt}    Residual {c |} {res}  2760.8494    41,786  .066071158   {txt}R-squared       ={res}    0.0041
{txt}{hline 13}{c +}{hline 34}   Adj R-squared   ={res}    0.0040
{txt}       Total {c |} {res} 2772.14671    41,788  .066338344   {txt}Root MSE        =   {res} .25704

{txt}{hline 13}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 1}   satis_dem{col 14}{c |} Coefficient{col 26}  Std. err.{col 38}      t{col 46}   P>|t|{col 54}     [95% con{col 67}f. interval]
{hline 13}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 8}wave {c |}
{space 10}3  {c |}{col 14}{res}{space 2}-.0196791{col 26}{space 2}   .00308{col 37}{space 1}   -6.39{col 46}{space 3}0.000{col 54}{space 4} -.025716{col 67}{space 3}-.0136423
{txt}{space 10}4  {c |}{col 14}{res}{space 2}-.0402712{col 26}{space 2}   .00308{col 37}{space 1}  -13.08{col 46}{space 3}0.000{col 54}{space 4}-.0463081{col 67}{space 3}-.0342344
{txt}{space 12} {c |}
{space 7}_cons {c |}{col 14}{res}{space 2} .5911838{col 26}{space 2} .0021779{col 37}{space 1}  271.45{col 46}{space 3}0.000{col 54}{space 4} .5869151{col 67}{space 3} .5954525
{txt}{hline 13}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{res}{txt}(52,391 missing values generated)
(52,391 missing values generated)
(52,391 missing values generated)
{res}{txt}1 2 3 4

{p 0 7}{space 1}{text:( 1)}{space 1} {res}2b.wave + _cons = 0{p_end}

{txt}{hline 13}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 1}   satis_dem{col 14}{c |} Coefficient{col 26}  Std. err.{col 38}      t{col 46}   P>|t|{col 54}     [95% con{col 67}f. interval]
{hline 13}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 9}(1) {c |}{col 14}{res}{space 2} .5911838{col 26}{space 2} .0021779{col 37}{space 1}  271.45{col 46}{space 3}0.000{col 54}{space 4} .5869151{col 67}{space 3} .5954525
{txt}{hline 13}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
(11,029 real changes made)
(11,029 real changes made)
(11,029 real changes made)

{p 0 7}{space 1}{text:( 1)}{space 1} {res}3.wave + _cons = 0{p_end}

{txt}{hline 13}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 1}   satis_dem{col 14}{c |} Coefficient{col 26}  Std. err.{col 38}      t{col 46}   P>|t|{col 54}     [95% con{col 67}f. interval]
{hline 13}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 9}(1) {c |}{col 14}{res}{space 2} .5715047{col 26}{space 2} .0021779{col 37}{space 1}  262.41{col 46}{space 3}0.000{col 54}{space 4}  .567236{col 67}{space 3} .5757734
{txt}{hline 13}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
(11,057 real changes made)
(11,057 real changes made)
(11,057 real changes made)

{p 0 7}{space 1}{text:( 1)}{space 1} {res}4.wave + _cons = 0{p_end}

{txt}{hline 13}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 1}   satis_dem{col 14}{c |} Coefficient{col 26}  Std. err.{col 38}      t{col 46}   P>|t|{col 54}     [95% con{col 67}f. interval]
{hline 13}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 9}(1) {c |}{col 14}{res}{space 2} .5509125{col 26}{space 2} .0021779{col 37}{space 1}  252.96{col 46}{space 3}0.000{col 54}{space 4} .5466438{col 67}{space 3} .5551813
{txt}{hline 13}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
(19,703 real changes made)
(19,703 real changes made)
(19,703 real changes made)

{com}. 
. keep coef* cimin* cimax* wave   
{txt}
{com}. duplicates drop

{p 0 4}{txt}Duplicates in terms of {txt} all variables{p_end}

(52,387 observations deleted)

{com}. 
. 
. 
. graph twoway (rcap cimax_satis_dem cimin_satis_dem  wave, lp(dash) lcolor("0 100 200 %85") m())  (connected  coef_satis_dem  wave, lp(longdash) msymbol(diamond) color("0 100 200") )  (rcap cimax_satis_head cimin_satis_head  wave,  lcolor("200 50 70 %85") lp(dash))  (connected  coef_satis_head  wave, lp(longdash) msymbol(diamond)  color("200 50 50")  )   ,  legend(order(2 "Satisfaction with" "democracy" 4 "Satisfaction with" "the head of government" ) on ring(0) pos(1) col(1) region(lstyle(none))  ) ///
> ytitle("Satisfaction level")  ylab(, nogrid) ///
> xlabel( 1 "March 26 2020" 2 "April 15 2020" 3 "June 16 2020" 4 "July 17 2020" , nogrid ) xtitle("Date") xscale(range(1 4.2))
{res}{txt}
{com}. graph export "3_output/2_OA/Figures/FigureF1.pdf", as(pdf) replace
{txt}{p 0 4 2}
file {bf}
3_output/2_OA/Figures/FigureF1.pdf{rm}
saved as
PDF
format
{p_end}

{com}. 
. 
. 
. **** back of the enveloppe computation 
. qui su coef_satis_dem if wave == 4
{txt}
{com}. local satis_dem_end = r(mean)
{txt}
{com}. qui su coef_satis_dem if wave == 2
{txt}
{com}. local satis_dem_beg = r(mean)
{txt}
{com}. 
. qui su coef_satis_head if wave == 4
{txt}
{com}. local satis_head_end = r(mean)
{txt}
{com}. qui su coef_satis_head if wave == 2
{txt}
{com}. local satis_head_beg = r(mean)
{txt}
{com}. 
. *** using most prefered estimate: 0.460 
. 
. local dif_dem = `satis_dem_end'-`satis_dem_beg'
{txt}
{com}. local dif_swg = `satis_head_end'-`satis_head_beg'
{txt}
{com}. 
. local explained = `dif_swg'*0.460
{txt}
{com}. local share_explained = `explained'/`dif_dem'
{txt}
{com}. di "`explained'"
{res}-.023682826757431
{txt}
{com}. di "`share_explained'"
{res}.5880831331524675
{txt}
{com}. 
{txt}end of do-file

{com}. * Note: this model is computationally expensive
. do "2_code/table_OA_heterogeneity.do"
{txt}
{com}. /*** *** *** *** *** *** *** *** *** *** *** *** *** *** *** *** *** ***
> Author: Daniel Stegmuller
> Date: February 2023
> Last modified: Sept 7 2023
> Object: Produce unobserved heterogeneity with correlated random effects table in the OA
> Databases in input: A_experiment.dta
> Databases in output: None
> 
> Tables generated in this script:
> 
> - Table E.13.: Instrumental variables estimates allowing for treatment effect heterogeneity in first and second stage.
> 
> *** *** *** *** *** *** *** *** *** *** *** *** *** *** *** *** *** ****/
. 
. 
. qui do 2_code/_ivcrc_estimator.ado
{txt}
{com}. qui do 2_code/ivcrc.ado
{txt}
{com}. 
. set matafavor speed
{txt}
{com}. set maxiter 40
{txt}
{com}. set seed 782526
{txt}
{com}. 
. * N bootstrap iter.
. local rep = 500
{txt}
{com}. 
. 
. 
. **************************************************************************
. * Satisfaction with the head of government
. **************************************************************************
. 
. use 1_data/A_experiment, replace
{txt}
{com}. 
. gen fifty = (fifties == 1| sixties == 1 | seventies == 1)
{txt}
{com}. local ctrl = "female income2quartile income3quartile income4quartile goodhealth fifty"
{txt}
{com}. 
. eststo clear 
{txt}
{com}. * M 1
. loc bw = 0.0195983
{txt}
{com}. eststo: ivcrc satis_head (eval_eco eval_sant = TH1_TE1 - TH4_TE4) `ctrl', ranks(5) kernel(epanechnikov) bandw(`bw') ave(0(0)1) bootstrap(rep(`rep') cluster(country))
{res}{txt}(running {bf:_ivcrc_estimator} on estimation sample)
{res}
{text}Bootstrap replications ({result:500}){text}: {res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}10{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}20{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}30{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}40{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}50{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}60{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}70{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}80{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}90{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}100{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}110{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}120{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}130{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}140{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}150{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}160{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}170{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}180{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}190{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}200{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}210{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}220{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}230{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}240{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}250{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}260{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}270{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}280{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}290{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}300{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}310{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}320{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}330{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}340{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}350{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}360{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}370{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}380{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}390{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}400{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}410{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}420{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}430{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}440{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}450{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}460{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}470{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}480{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}490{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}500{text} done
{res}
{txt}IVCRC{col 49}Number of obs{col 67}= {res}    22,530
{txt}{col 49}Replications{col 67}= {res}       500
{col 1}{text}{hline 13}{c TT}{hline 12}{hline 11}{hline 9}{hline 10}{hline 11}{hline 11}
{col 1}{text}  satis_head{col 14}{c |}     Coef.{col 27} Std. Err.{col 38}    z{col 47}  P>|z|{col 57}  [95% Conf. Interval]
{res}{col 1}{text}{hline 13}{c +}{hline 12}{hline 11}{hline 9}{hline 10}{hline 11}{hline 11}
{col 1}{text}    eval_eco{col 14}{c |}{result}{space 2} .4148725{col 27} .0343653{col 38}  12.07{col 47}  0.000{col 57}{space 2} .3475178{col 68}{space 2} .4822273
{col 1}{text}   eval_sant{col 14}{c |}{result}{space 2}    .5847{col 27} .0362356{col 38}  16.14{col 47}  0.000{col 57}{space 2} .5136796{col 68}{space 2} .6557204
{col 1}{text}      female{col 14}{c |}{result}{space 2} .0093919{col 27} .0055206{col 38}   1.70{col 47}  0.089{col 57}{space 2}-.0014284{col 68}{space 2} .0202121
{col 1}{text}income2qua~e{col 14}{c |}{result}{space 2}-.0042568{col 27} .0058309{col 38}  -0.73{col 47}  0.465{col 57}{space 2}-.0156852{col 68}{space 2} .0071716
{col 1}{text}income3qua~e{col 14}{c |}{result}{space 2}-.0010634{col 27} .0048802{col 38}  -0.22{col 47}  0.828{col 57}{space 2}-.0106285{col 68}{space 2} .0085017
{col 1}{text}income4qua~e{col 14}{c |}{result}{space 2}-.0035116{col 27} .0049673{col 38}  -0.71{col 47}  0.480{col 57}{space 2}-.0132473{col 68}{space 2} .0062241
{col 1}{text}  goodhealth{col 14}{c |}{result}{space 2} .0074194{col 27} .0047045{col 38}   1.58{col 47}  0.115{col 57}{space 2}-.0018012{col 68}{space 2} .0166399
{col 1}{text}       fifty{col 14}{c |}{result}{space 2}-.0153195{col 27} .0086802{col 38}  -1.76{col 47}  0.078{col 57}{space 2}-.0323325{col 68}{space 2} .0016935
{col 1}{text}       _cons{col 14}{c |}{result}{space 2} -.048104{col 27} .0097353{col 38}  -4.94{col 47}  0.000{col 57}{space 2}-.0671848{col 68}{space 2}-.0290232
{col 1}{text}{hline 13}{c BT}{hline 12}{hline 11}{hline 9}{hline 10}{hline 11}{hline 11}
Note: {bf:Bandwidth = .0195983}
{res}{txt}({res}est1{txt} stored)

{com}. estadd scalar bw = `bw'

{txt}added scalar:
                 e(bw) =  {res}.0195983
{txt}
{com}. estadd local iv "16 IVs"

{txt}added macro:
                 e(iv) : "{res:16 IVs}"

{com}. 
. * M 2
. loc bw = 0.0268942
{txt}
{com}. eststo: ivcrc satis_head (eval_eco eval_sant = econc healthc) `ctrl', ranks(5) kernel(epanechnikov) bandw(`bw') ave(0(0)1) bootstrap(rep(`rep') cluster(country))
{res}{txt}(running {bf:_ivcrc_estimator} on estimation sample)
{res}
{text}Bootstrap replications ({result:500}){text}: {res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}10{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}20{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}30{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}40{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}50{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}60{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}70{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}80{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}90{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}100{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}110{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}120{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}130{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}140{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}150{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}160{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}170{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}180{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}190{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}200{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}210{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}220{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}230{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}240{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}250{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}260{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}270{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}280{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}290{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}300{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}310{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}320{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}330{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}340{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}350{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}360{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}370{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}380{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}390{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}400{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}410{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}420{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}430{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}440{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}450{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}460{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}470{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}480{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}490{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}500{text} done
{res}
{txt}IVCRC{col 49}Number of obs{col 67}= {res}    22,530
{txt}{col 49}Replications{col 67}= {res}       500
{col 1}{text}{hline 13}{c TT}{hline 12}{hline 11}{hline 9}{hline 10}{hline 11}{hline 11}
{col 1}{text}  satis_head{col 14}{c |}     Coef.{col 27} Std. Err.{col 38}    z{col 47}  P>|z|{col 57}  [95% Conf. Interval]
{res}{col 1}{text}{hline 13}{c +}{hline 12}{hline 11}{hline 9}{hline 10}{hline 11}{hline 11}
{col 1}{text}    eval_eco{col 14}{c |}{result}{space 2} .4143771{col 27} .0377056{col 38}  10.99{col 47}  0.000{col 57}{space 2} .3404756{col 68}{space 2} .4882787
{col 1}{text}   eval_sant{col 14}{c |}{result}{space 2} .5848824{col 27} .0391546{col 38}  14.94{col 47}  0.000{col 57}{space 2} .5081408{col 68}{space 2}  .661624
{col 1}{text}      female{col 14}{c |}{result}{space 2} .0093762{col 27} .0054403{col 38}   1.72{col 47}  0.085{col 57}{space 2}-.0012865{col 68}{space 2}  .020039
{col 1}{text}income2qua~e{col 14}{c |}{result}{space 2}-.0045675{col 27} .0059383{col 38}  -0.77{col 47}  0.442{col 57}{space 2}-.0162064{col 68}{space 2} .0070714
{col 1}{text}income3qua~e{col 14}{c |}{result}{space 2}-.0012635{col 27}  .005439{col 38}  -0.23{col 47}  0.816{col 57}{space 2}-.0119238{col 68}{space 2} .0093968
{col 1}{text}income4qua~e{col 14}{c |}{result}{space 2}-.0037407{col 27} .0054191{col 38}  -0.69{col 47}  0.490{col 57}{space 2} -.014362{col 68}{space 2} .0068805
{col 1}{text}  goodhealth{col 14}{c |}{result}{space 2} .0074292{col 27} .0049513{col 38}   1.50{col 47}  0.133{col 57}{space 2}-.0022752{col 68}{space 2} .0171336
{col 1}{text}       fifty{col 14}{c |}{result}{space 2}-.0153194{col 27}  .009257{col 38}  -1.65{col 47}  0.098{col 57}{space 2}-.0334628{col 68}{space 2}  .002824
{col 1}{text}       _cons{col 14}{c |}{result}{space 2}-.0477362{col 27} .0108887{col 38}  -4.38{col 47}  0.000{col 57}{space 2}-.0690776{col 68}{space 2}-.0263948
{col 1}{text}{hline 13}{c BT}{hline 12}{hline 11}{hline 9}{hline 10}{hline 11}{hline 11}
Note: {bf:Bandwidth = .0268942}
{res}{txt}({res}est2{txt} stored)

{com}. estadd scalar bw = `bw'

{txt}added scalar:
                 e(bw) =  {res}.0268942
{txt}
{com}. estadd local iv "2 SumIVs"

{txt}added macro:
                 e(iv) : "{res:2 SumIVs}"

{com}. 
. 
. **************************************************************************
. * Satisfaction with democracy
. **************************************************************************
. 
. use 1_data/A_experiment, replace
{txt}
{com}. gen fifty = (fifties == 1| sixties == 1 | seventies == 1)
{txt}
{com}. local ctrl = "female income2quartile income3quartile income4quartile goodhealth fifty"
{txt}
{com}. 
. * M 3
. loc bw = 0.0192313
{txt}
{com}. eststo: ivcrc satis_dem (satis_head = TH1_TE1 - TH4_TE4) `ctrl', ranks(5) kernel(epanechnikov) bandw(`bw') bootstrap(rep(`rep') cluster(country))
{res}{txt}(running {bf:_ivcrc_estimator} on estimation sample)
{res}
{text}Bootstrap replications ({result:500}){text}: {res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}10{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}20{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}30{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}40{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}50{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}60{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}70{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}80{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}90{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}100{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}110{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}120{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}130{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}140{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}150{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}160{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}170{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}180{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}190{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}200{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}210{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}220{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}230{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}240{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}250{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}260{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}270{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}280{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}290{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}300{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}310{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}320{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}330{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}340{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}350{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}360{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}370{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}380{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}390{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}400{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}410{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}420{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}430{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}440{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}450{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}460{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}470{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}480{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}490{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}500{text} done
{res}
{txt}IVCRC{col 49}Number of obs{col 67}= {res}    22,530
{txt}{col 49}Replications{col 67}= {res}       500
{col 1}{text}{hline 13}{c TT}{hline 12}{hline 11}{hline 9}{hline 10}{hline 11}{hline 11}
{col 1}{text}   satis_dem{col 14}{c |}     Coef.{col 27} Std. Err.{col 38}    z{col 47}  P>|z|{col 57}  [95% Conf. Interval]
{res}{col 1}{text}{hline 13}{c +}{hline 12}{hline 11}{hline 9}{hline 10}{hline 11}{hline 11}
{col 1}{text}  satis_head{col 14}{c |}{result}{space 2} .5266697{col 27}  .071015{col 38}   7.42{col 47}  0.000{col 57}{space 2} .3874829{col 68}{space 2} .6658565
{col 1}{text}      female{col 14}{c |}{result}{space 2}-.0103064{col 27} .0062905{col 38}  -1.64{col 47}  0.101{col 57}{space 2}-.0226354{col 68}{space 2} .0020227
{col 1}{text}income2qua~e{col 14}{c |}{result}{space 2} .0193025{col 27}  .005338{col 38}   3.62{col 47}  0.000{col 57}{space 2} .0088403{col 68}{space 2} .0297647
{col 1}{text}income3qua~e{col 14}{c |}{result}{space 2} .0255874{col 27} .0054376{col 38}   4.71{col 47}  0.000{col 57}{space 2} .0149298{col 68}{space 2} .0362449
{col 1}{text}income4qua~e{col 14}{c |}{result}{space 2} .0456811{col 27}  .006288{col 38}   7.26{col 47}  0.000{col 57}{space 2} .0333568{col 68}{space 2} .0580055
{col 1}{text}  goodhealth{col 14}{c |}{result}{space 2} .0290294{col 27}  .006279{col 38}   4.62{col 47}  0.000{col 57}{space 2} .0167229{col 68}{space 2}  .041336
{col 1}{text}       fifty{col 14}{c |}{result}{space 2}  .013684{col 27} .0055592{col 38}   2.46{col 47}  0.014{col 57}{space 2} .0027881{col 68}{space 2} .0245799
{col 1}{text}       _cons{col 14}{c |}{result}{space 2} .2495432{col 27} .0326486{col 38}   7.64{col 47}  0.000{col 57}{space 2} .1855531{col 68}{space 2} .3135334
{col 1}{text}{hline 13}{c BT}{hline 12}{hline 11}{hline 9}{hline 10}{hline 11}{hline 11}
Note: Average coefficients over {bf:R = [0,1]} rank subset; {bf:Bandwidth = .0192313}
{res}{txt}({res}est3{txt} stored)

{com}. estadd scalar bw = `bw'

{txt}added scalar:
                 e(bw) =  {res}.0192313
{txt}
{com}. estadd local iv "16 IVs"

{txt}added macro:
                 e(iv) : "{res:16 IVs}"

{com}. 
. * M 4
. loc bw = 0.016551
{txt}
{com}. eststo: ivcrc satis_dem (satis_head = econc healthc) `ctrl', ranks(5) kernel(epanechnikov) bandw(`bw') bootstrap(rep(`rep') cluster(country))
{res}{txt}(running {bf:_ivcrc_estimator} on estimation sample)
{res}
{text}Bootstrap replications ({result:500}){text}: {res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}10{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}20{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}30{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}40{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}50{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}60{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}70{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}80{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}90{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}100{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}110{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}120{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}130{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}140{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}150{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}160{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}170{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}180{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}190{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}200{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}210{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}220{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}230{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}240{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}250{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}260{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}270{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}280{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}290{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}300{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}310{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}320{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}330{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}340{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}350{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}360{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}370{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}380{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}390{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}400{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}410{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}420{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}430{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}440{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}450{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}460{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}470{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}480{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}490{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}500{text} done
{res}
{txt}IVCRC{col 49}Number of obs{col 67}= {res}    22,530
{txt}{col 49}Replications{col 67}= {res}       500
{col 1}{text}{hline 13}{c TT}{hline 12}{hline 11}{hline 9}{hline 10}{hline 11}{hline 11}
{col 1}{text}   satis_dem{col 14}{c |}     Coef.{col 27} Std. Err.{col 38}    z{col 47}  P>|z|{col 57}  [95% Conf. Interval]
{res}{col 1}{text}{hline 13}{c +}{hline 12}{hline 11}{hline 9}{hline 10}{hline 11}{hline 11}
{col 1}{text}  satis_head{col 14}{c |}{result}{space 2} .5406735{col 27} .0930134{col 38}   5.81{col 47}  0.000{col 57}{space 2} .3583706{col 68}{space 2} .7229763
{col 1}{text}      female{col 14}{c |}{result}{space 2}-.0093207{col 27} .0074879{col 38}  -1.24{col 47}  0.213{col 57}{space 2}-.0239966{col 68}{space 2} .0053552
{col 1}{text}income2qua~e{col 14}{c |}{result}{space 2} .0198433{col 27} .0052272{col 38}   3.80{col 47}  0.000{col 57}{space 2} .0095982{col 68}{space 2} .0300885
{col 1}{text}income3qua~e{col 14}{c |}{result}{space 2} .0249767{col 27} .0051459{col 38}   4.85{col 47}  0.000{col 57}{space 2} .0148908{col 68}{space 2} .0350625
{col 1}{text}income4qua~e{col 14}{c |}{result}{space 2} .0429723{col 27} .0063617{col 38}   6.75{col 47}  0.000{col 57}{space 2} .0305036{col 68}{space 2}  .055441
{col 1}{text}  goodhealth{col 14}{c |}{result}{space 2}   .02989{col 27} .0069137{col 38}   4.32{col 47}  0.000{col 57}{space 2} .0163394{col 68}{space 2} .0434405
{col 1}{text}       fifty{col 14}{c |}{result}{space 2}  .013174{col 27} .0061726{col 38}   2.13{col 47}  0.033{col 57}{space 2}  .001076{col 68}{space 2}  .025272
{col 1}{text}       _cons{col 14}{c |}{result}{space 2} .2491175{col 27} .0377907{col 38}   6.59{col 47}  0.000{col 57}{space 2} .1750491{col 68}{space 2}  .323186
{col 1}{text}{hline 13}{c BT}{hline 12}{hline 11}{hline 9}{hline 10}{hline 11}{hline 11}
Note: Average coefficients over {bf:R = [0,1]} rank subset; {bf:Bandwidth = .016551}
{res}{txt}({res}est4{txt} stored)

{com}. estadd scalar bw = `bw'

{txt}added scalar:
                 e(bw) =  {res}.016551
{txt}
{com}. estadd local iv "2 SumIVs"

{txt}added macro:
                 e(iv) : "{res:2 SumIVs}"

{com}. 
. * M 5
. loc bw = 0.0163449
{txt}
{com}. eststo: ivcrc satis_dem (satis_head = treatc) `ctrl', ranks(5) kernel(epanechnikov) bandw(`bw') bootstrap(rep(`rep') cluster(country))
{res}{txt}(running {bf:_ivcrc_estimator} on estimation sample)
{res}
{text}Bootstrap replications ({result:500}){text}: {res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}10{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}20{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}30{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}40{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}50{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}60{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}70{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}80{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}90{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}100{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}110{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}120{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}130{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}140{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}150{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}160{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}170{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}180{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}190{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}200{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}210{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}220{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}230{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}240{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}250{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}260{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}270{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}280{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}290{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}300{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}310{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}320{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}330{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}340{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}350{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}360{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}370{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}380{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}390{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}400{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}410{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}420{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}430{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}440{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}450{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}460{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}470{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}480{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}490{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}500{text} done
{res}
{txt}IVCRC{col 49}Number of obs{col 67}= {res}    22,530
{txt}{col 49}Replications{col 67}= {res}       500
{col 1}{text}{hline 13}{c TT}{hline 12}{hline 11}{hline 9}{hline 10}{hline 11}{hline 11}
{col 1}{text}   satis_dem{col 14}{c |}     Coef.{col 27} Std. Err.{col 38}    z{col 47}  P>|z|{col 57}  [95% Conf. Interval]
{res}{col 1}{text}{hline 13}{c +}{hline 12}{hline 11}{hline 9}{hline 10}{hline 11}{hline 11}
{col 1}{text}  satis_head{col 14}{c |}{result}{space 2} .5491355{col 27} .0923526{col 38}   5.95{col 47}  0.000{col 57}{space 2} .3681278{col 68}{space 2} .7301432
{col 1}{text}      female{col 14}{c |}{result}{space 2}-.0093567{col 27} .0073929{col 38}  -1.27{col 47}  0.206{col 57}{space 2}-.0238465{col 68}{space 2} .0051331
{col 1}{text}income2qua~e{col 14}{c |}{result}{space 2} .0197314{col 27} .0048777{col 38}   4.05{col 47}  0.000{col 57}{space 2} .0101712{col 68}{space 2} .0292916
{col 1}{text}income3qua~e{col 14}{c |}{result}{space 2} .0248081{col 27} .0055394{col 38}   4.48{col 47}  0.000{col 57}{space 2}  .013951{col 68}{space 2} .0356651
{col 1}{text}income4qua~e{col 14}{c |}{result}{space 2} .0425421{col 27} .0063889{col 38}   6.66{col 47}  0.000{col 57}{space 2} .0300201{col 68}{space 2} .0550641
{col 1}{text}  goodhealth{col 14}{c |}{result}{space 2} .0291772{col 27} .0067699{col 38}   4.31{col 47}  0.000{col 57}{space 2} .0159085{col 68}{space 2} .0424459
{col 1}{text}       fifty{col 14}{c |}{result}{space 2} .0135202{col 27} .0056213{col 38}   2.41{col 47}  0.016{col 57}{space 2} .0025025{col 68}{space 2} .0245378
{col 1}{text}       _cons{col 14}{c |}{result}{space 2} .2464548{col 27} .0360977{col 38}   6.83{col 47}  0.000{col 57}{space 2} .1757047{col 68}{space 2}  .317205
{col 1}{text}{hline 13}{c BT}{hline 12}{hline 11}{hline 9}{hline 10}{hline 11}{hline 11}
Note: Average coefficients over {bf:R = [0,1]} rank subset; {bf:Bandwidth = .0163449}
{res}{txt}({res}est5{txt} stored)

{com}. estadd scalar bw = `bw'

{txt}added scalar:
                 e(bw) =  {res}.0163449
{txt}
{com}. estadd local iv "SumIV"

{txt}added macro:
                 e(iv) : "{res:SumIV}"

{com}. 
. 
. esttab  using "3_output/2_OA/Tables/TableE13.tex", cells(b(star fmt(%5.3fc)) se(par fmt(%5.3fc)))  varlabels(eval_eco "Economic satisfaction" eval_sant "Health satisfaction" satis_head "Satisfaction with the head of government") keep(eval_eco eval_sant satis_head) stats(iv bw N, fmt(%15s %5.4f %9.0fc) labels("Instruments" "Bandwidth \emph{c -(}h{c )-}" "Observations")) numbers mlabels(,none) collabels(,none) eqlabels(, none)  mgroups("Satisfaction with the head of government" "Satisfaction with democracy", pattern(1 0 1 0 0 ) span prefix(\multicolumn{c -(}@span{c )-}{c -(}c{c )-}{c -(}) suffix({c )-}) erepeat(\cmidrule(lr){c -(}@span{c )-})) replace star(* 0.10 ** 0.05 *** 0.01)
{res}{txt}{p 0 4 2}
(file {bf}
3_output/2_OA/Tables/TableE13.tex{rm}
not found)
{p_end}
(output written to {browse  `"3_output/2_OA/Tables/TableE13.tex"'})

{com}. 
{txt}end of do-file

{com}. 
. log close 
      {txt}name:  {res}<unnamed>
       {txt}log:  {res}/Users/dstegmue/Library/CloudStorage/Dropbox/COVID_experiment_replication/PUBLISH/2_code/replication_log.smcl
  {txt}log type:  {res}smcl
 {txt}closed on:  {res} 8 Sep 2023, 13:24:05
{txt}{.-}
{smcl}
{txt}{sf}{ul off}